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Journal articles on the topic 'Image quality enhancement'
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Abebe, Mekides Assefa, and Jon Yngve Hardeberg. "Deep Learning Approaches for Whiteboard Image Quality Enhancement." Color and Imaging Conference 2019, no. 1 (October 21, 2019): 360–68. http://dx.doi.org/10.2352/j.imagingsci.technol.2019.63.4.040404.
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Different whiteboard image degradations highly reduce the legibility of pen-stroke content as well as the overall quality of the images. Consequently, different researchers addressed the problem through different image enhancement techniques. Most of the state-of-the-art approaches applied common image processing techniques such as background foreground segmentation, text extraction, contrast and color enhancements and white balancing. However, such types of conventional enhancement methods are incapable of recovering severely degraded pen-stroke contents and produce artifacts in the presence of complex pen-stroke illustrations. In order to surmount such problems, the authors have proposed a deep learning based solution. They have contributed a new whiteboard image data set and adopted two deep convolutional neural network architectures for whiteboard image quality enhancement applications. Their different evaluations of the trained models demonstrated their superior performances over the conventional methods.
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Zhai, Guangtao, Wei Sun, Xiongkuo Min, and Jiantao Zhou. "Perceptual Quality Assessment of Low-light Image Enhancement." ACM Transactions on Multimedia Computing, Communications, and Applications 17, no. 4 (November 30, 2021): 1–24. http://dx.doi.org/10.1145/3457905.
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Low-light image enhancement algorithms (LIEA) can light up images captured in dark or back-lighting conditions. However, LIEA may introduce various distortions such as structure damage, color shift, and noise into the enhanced images. Despite various LIEAs proposed in the literature, few efforts have been made to study the quality evaluation of low-light enhancement. In this article, we make one of the first attempts to investigate the quality assessment problem of low-light image enhancement. To facilitate the study of objective image quality assessment (IQA), we first build a large-scale low-light image enhancement quality (LIEQ) database. The LIEQ database includes 1,000 light-enhanced images, which are generated from 100 low-light images using 10 LIEAs. Rather than evaluating the quality of light-enhanced images directly, which is more difficult, we propose to use the multi-exposure fused (MEF) image and stack-based high dynamic range (HDR) image as a reference and evaluate the quality of low-light enhancement following a full-reference (FR) quality assessment routine. We observe that distortions introduced in low-light enhancement are significantly different from distortions considered in traditional image IQA databases that are well-studied, and the current state-of-the-art FR IQA models are also not suitable for evaluating their quality. Therefore, we propose a new FR low-light image enhancement quality assessment (LIEQA) index by evaluating the image quality from four aspects: luminance enhancement, color rendition, noise evaluation, and structure preserving, which have captured the most key aspects of low-light enhancement. Experimental results on the LIEQ database show that the proposed LIEQA index outperforms the state-of-the-art FR IQA models. LIEQA can act as an evaluator for various low-light enhancement algorithms and systems. To the best of our knowledge, this article is the first of its kind comprehensive low-light image enhancement quality assessment study.
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Attia, Salim J. "Assessment of Some Enhancement Methods of Renal X-ray Image." NeuroQuantology 18, no. 12 (December 31, 2020): 01–05. http://dx.doi.org/10.14704/nq.2020.18.12.nq20231.
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The study focuses on assessment of the quality of some image enhancement methods which were implemented on renal X-ray images. The enhancement methods included Imadjust, Histogram Equalization (HE) and Contrast Limited Adaptive Histogram Equalization (CLAHE). The images qualities were calculated to compare input images with output images from these three enhancement techniques. An eight renal x-ray images are collected to perform these methods. Generally, the x-ray images are lack of contrast and low in radiation dosage. This lack of image quality can be amended by enhancement process. Three quality image factors were done to assess the resulted images involved (Naturalness Image Quality Evaluator (NIQE), Perception based Image Quality Evaluator (PIQE) and Blind References Image Spatial Quality Evaluator (BRISQE)). The quality of images had been heightened by these methods to support the goals of diagnosis. The results of the chosen enhancement methods of collecting images reflected more qualified images than the original images. According to the results of the quality factors and the assessment of radiology experts, the CLAHE method was the best enhancement method.
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Baqer, Ismail Sh. "Image Quality Enhancing by Efficient Histogram Equalization." Wasit Journal of Engineering Sciences 2, no. 2 (October 2, 2014): 47–58. http://dx.doi.org/10.31185/ejuow.vol2.iss2.29.
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A two Level Image Quality enhancement is proposed in this paper. In the first level, Dualistic Sub-Image Histogram Equalization DSIHE method decomposes the original image into two sub-images based on median of original images. The second level deals with spikes shaped noise that may appear in the image after processing. We presents three methods of image enhancement GHE, LHE and proposed DSIHE that improve the visual quality of images. A comparative calculations is being carried out on above mentioned techniques to examine objective and subjective image quality parameters e.g. Peak Signal-to-Noise Ratio PSNR values, entropy H and mean squared error MSE to measure the quality of gray scale enhanced images.
For handling gray-level images, convenient Histogram Equalization methods e.g. GHE and LHE tend to change the mean brightness of an image to middle level of the gray-level range limiting their appropriateness for contrast enhancement in consumer electronics such as TV monitors. The DSIHE methods seem to overcome this disadvantage as they tend to preserve both, the brightness and contrast enhancement. Experimental results show that the proposed technique gives better results in terms of Discrete Entropy, Signal to Noise ratio and Mean Squared Error values than the Global and Local histogram-based equalization methods
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Ibrahim, Nuha Jameel, Yossra Hussain Ali, and Tarik Ahmed Rashid. "Intelligent Image Enhancement System based on Similarity Pixels." Webology 19, no. 1 (January 20, 2022): 1731–49. http://dx.doi.org/10.14704/web/v19i1/web19116.
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The main goal of image enhancement is to enhance the fine details present in the images having low luminance for better image quality. In the digital image processing field, the enhancement and removing the noise from the image is a critical issue; image noise removal is the manipulation of the image data to produce a visually high-quality image. The important details and useful information on image decreasing by the noise where the noise treated as information. The filters are used to remove unwanted information. The filters’ objectives are to improve the image quality. This paper proposes an enhancement image system, which chooses the appropriate filter and value of center pixel depends on the number of similarities adjusted neighbors pixels to the center pixel. The performance of this system is evaluated by using different quality metrics, such as Mean square error (MSE), Peak Signal Noise to Ratio (PSNR), Absolute Mean Brightness Error (AMBE), Measure of Enhancement (EME), and Measure of Enhancement by Entropy (EMEE), Entropy, Second-Order Entropy (SOE), and Image Enhancement Metric (IEM). The proposed enhancement system is efficient in removing noises and enhancing the image quality. Experiments are applied to a set of images, such as Lena, butterfly, etc. with different image sizes. The results show that the enhancement quality was performed well in the proposed system with minimal unexpected artifacts as compared to the other techniques, where the results of the proposed system for MSE, PSNR, AMBE, Entropy, SOE, EME, EMEE, and IEM for baboon image with the size 255x 255 are 2.906, 8.875, 3.92, 5.154, 2.692, 3.915, 0.442 and 3.674 in sequence.
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Gupta, Pooja, and Kuldip Pahwa. "Clock Algorithm Analysis for Increasing Quality of Digital Images." International Journal of Image and Graphics 16, no. 03 (July 2016): 1650016. http://dx.doi.org/10.1142/s0219467816500169.
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A digital image is not an exact snapshot of reality; it is only a discrete approximation. Thus, the captured images are always bit different from the images actually perceived by human eyes. These variations occur due to varying lighting conditions, weathers conditions like rain and fog, distance of scene from camera, image capturing angle, etc. The problem becomes more severe if these images are captured using low resolution image capturing devices like: Mobile phones, CCTV Cameras, Webcam, VGA cameras etc. Image enhancement addresses a solution of generating a high quality image from its low contrast version. Color enhancement is a process that differentiates objects in an image; as well as provides the detailed information of that image. This paper proposes color enhancement of low resolution digital images using clock algorithm. It is claimed that the proposed clock algorithm employed here produces good quality images in comparison with the existing color enhancement techniques. The simulation results proved that the proposed clock algorithm efficiently enhances the quality of digital low resolution images and analytically their quality improvement is observed in terms of peak signal to noise ratio (PSNR), mean square error (MSE) and bit error rate (BER) over the existing color enhancement techniques.
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Park, So Yeon, and Byung Cheol Song. "Image Quality Enhancement for Chest X-ray images." Journal of the Institute of Electronics and Information Engineers 52, no. 10 (October 25, 2015): 97–107. http://dx.doi.org/10.5573/ieie.2015.52.10.097.
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Li, Wenxia, Chi Lin, Ting Luo, Hong Li, Haiyong Xu, and Lihong Wang. "Subjective and Objective Quality Evaluation for Underwater Image Enhancement and Restoration." Symmetry 14, no. 3 (March 10, 2022): 558. http://dx.doi.org/10.3390/sym14030558.
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Since underwater imaging is affected by the complex water environment, it often leads to severe distortion of the underwater image. To improve the quality of underwater images, underwater image enhancement and restoration methods have been proposed. However, many underwater image enhancement and restoration methods produce over-enhancement or under-enhancement, which affects their application. To better design underwater image enhancement and restoration methods, it is necessary to research the underwater image quality evaluation (UIQE) for underwater image enhancement and restoration methods. Therefore, a subjective evaluation dataset for an underwater image enhancement and restoration method is constructed, and on this basis, an objective quality evaluation method of underwater images, based on the relative symmetry of underwater dark channel prior (UDCP) and the underwater bright channel prior (UBCP) is proposed. Specifically, considering underwater image enhancement in different scenarios, a UIQE dataset is constructed, which contains 405 underwater images, generated from 45 different underwater real images, using 9 representative underwater image enhancement methods. Then, a subjective quality evaluation of the UIQE database is studied. To quantitatively measure the quality of the enhanced and restored underwater images with different characteristics, an objective UIQE index (UIQEI) is used, by extracting and fusing four groups of features, including: (1) the joint statistics of normalized gradient magnitude (GM) and Laplacian of Gaussian (LOG) features, based on the underwater dark channel map; (2) the joint statistics of normalized gradient magnitude (GM) and Laplacian of Gaussian (LOG) features, based on the underwater bright channel map; (3) the saturation and colorfulness features; (4) the fog density feature; (5) the global contrast feature; these features capture key aspects of underwater images. Finally, the experimental results are analyzed, qualitatively and quantitatively, to illustrate the effectiveness of the proposed UIQEI method.
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Wang, Qiu Yun. "Depth Estimation Based Underwater Image Enhancement." Advanced Materials Research 926-930 (May 2014): 1704–7. http://dx.doi.org/10.4028/www.scientific.net/amr.926-930.1704.
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According to the image formation model and the nature of underwater images, we find that the effect of the haze and the color distortion seriously pollute the underwater image data, lowing the quality of the underwater images in the visibility and the quality of the data. Hence, aiming to reduce the noise and the haze effect existing in the underwater image and compensate the color distortion, the dark channel prior model is used to enhance the underwater image. We compare the dark channel prior model based image enhancement method to the contrast stretching based method for image enhancement. The experimental results proved that the dark channel prior model has good ability for processing the underwater images. The super performance of the proposed method is demonstrated as well.
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Cui, Fa Yi, and Lei Lei Ma. "Adaptive Image Generalized Fuzzy Enhancement and Quality Evaluation." Applied Mechanics and Materials 590 (June 2014): 736–40. http://dx.doi.org/10.4028/www.scientific.net/amm.590.736.
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Before image feature detection and recognition, image enhancement can highlight the main people or things and their details from foreground, and also can suppress the useless information from background effectively. An algorithm model of adaptive image generalized fuzzy enhancement is established. For all aspects of the algorithm model, a variety of computing forms are put forward, and the evaluation standard of image quality is defined. The principle of algorithm is to achieve space transform between image gray space and generalized fuzzy space using generalized membership transform and its adverse transform. In the process of space transform, the contrast among successive region for space of generalized fuzzy membership grade is enhanced by generalized fuzzy enhancement function. Enhanced images are evaluated by quality standard, and the optimal values of adjustable parameters of membership grade transformation function and the fuzzy enhancement function are selected adaptively based on the optimal quality. Then, the enhanced image with best quality can be obtained. Experiments show that the extracted contour of enhanced image is structured, weak-edge-highlighting, and rich-detail.
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Panetta, Karen, Arash Samani, and Sos Agaian. "Choosing the Optimal Spatial Domain Measure of Enhancement for Mammogram Images." International Journal of Biomedical Imaging 2014 (2014): 1–8. http://dx.doi.org/10.1155/2014/937849.
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Medical imaging systems often require image enhancement, such as improving the image contrast, to provide medical professionals with the best visual image quality. This helps in anomaly detection and diagnosis. Most enhancement algorithms are iterative processes that require many parameters be selected. Poor or nonoptimal parameter selection can have a negative effect on the enhancement process. In this paper, a quantitative metric for measuring the image quality is used to select the optimal operating parameters for the enhancement algorithms. A variety of measures evaluating the quality of an image enhancement will be presented along with each measure’s basis for analysis, namely, on image content and image attributes. We also provide guidelines for systematically choosing the proper measure of image quality for medical images.
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Irshad, Muhammad, Alessandro R. Silva, Sana Alamgeer, and Mylène C. Q. Farias. "Perceptual Quality Assessment of Enhanced Images Using a Crowd-Sourcing Framework." Electronic Imaging 2020, no. 9 (January 26, 2020): 66–1. http://dx.doi.org/10.2352/issn.2470-1173.2020.9.iqsp-066.
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In this work, we present a psychophysical study, in which, we analyzed the perceptual quality of images enhanced with several types of enhancement algorithms, including color, sharpness, histogram, and contrast enhancements. To estimate and compare the qualities of enhanced images, we performed a psychophysical experiment with 35 source images, obtained from publicly available databases. More specifically, we used images from the Challenge Database, the CSIQ database, and the TID2013 database. To generate the test sequences, we used 12 different image enhancement algorithms, generating a dataset with a total of 455 images. We used a Double Stimulus Continuous Quality Scale (DSCQS) experimental methodology, with a between-subjects approach where each subject scored a subset of the total database to avoid fatigue. Given the high number of test images, we designed a crowd-sourcing interface to perform an online psychophysical experiment. This type of interface has the advantage of making it possible to collect data from many participants. We also performed an experiment in a controlled laboratory environment and compared its results with the crowd-sourcing results. Since there are very few quality enhancement databases available in the literature, this works represents a contribution to the area of image quality.
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Sharma, Puspad Kumar, Nitesh Gupta, and Anurag Shrivastava. "A Review on Deep Image Contrast Enhancement." SMART MOVES JOURNAL IJOSCIENCE 6, no. 1 (January 8, 2020): 4. http://dx.doi.org/10.24113/ijoscience.v6i1.258.
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In image processing applications, one of the main preprocessing phases is image enhancement that is used to produce high quality image or enhanced image than the original input image. These enhanced images can be used in many applications such as remote sensing applications, geo-satellite images, etc. The quality of an image is affected due to several conditions such as by poor illumination, atmospheric condition, wrong lens aperture setting of the camera, noise, etc [2]. So, such degraded/low exposure images are needed to be enhanced by increasing the brightness as well as its contrast and this can be possible by the method of image enhancement. In this research work different image enhancement techniques are discussed and reviewed with their results. The aim of this study is to determine the application of deep learning approaches that have been used for image enhancement. Deep learning is a machine learning approach which is currently revolutionizing a number of disciplines including image processing and computer vision. This paper will attempt to apply deep learning to image filtering, specifically low-light image enhancement. The review given in this paper is quite efficient for future researchers to overcome problems that helps in designing efficient algorithm which enhances quality of the image.
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Vinoothna, Boppudi. "Design and Development of Contrast-Limited Adaptive Histogram Equalization Technique for Enhancing MRI Images by Improving PSNR, UIQI Parameters in Comparison with Median Filtering." ECS Transactions 107, no. 1 (April 24, 2022): 14819–27. http://dx.doi.org/10.1149/10701.14819ecst.
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Image enhancement is used to improve the quality of images and it enhances, sharpens image features, such as edges, boundaries, and contrast, to make a graphic display useful for display and analysis. In order to enhance the quality of MRI images, histogram-based image enhancement technique is developed in this work. Materials and Methods: In this research, a Contrast Limited Adaptive Histogram Equalization (CLAHE) based image enhancement technique is proposed and developed for MRI images and the proposed work is compared with another image enhancement technique called Median Filtering (MF) method. Input medical images (N=30) of both group were downloaded from standard medical database. The enrollment ratio is obtained as 1 with 95% confidence interval and a threshold value 0.05. Results: The performance of image enhancement is measured using two parameters namely, Peak Signal Noise Ratio (PSNR) and Universal Image Quality Index (UIQI). These parameters are calculated and evaluated to assess the proposed methods efficacy. High values of PSNR and UIQI indicate better enhancement. CLAHE provides mean PSNR values of 18.6968(dB), mean UIQI of 80.9220%, and median filtering method provides mean PSNR values of 14.2261(dB) and mean UIQI of 76.3463%. Conclusion: Based on the experiment's results, the CLAHE image enhancement technique significantly performed better than the MF technique.
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Abbood, Alaa Ahmed, Mohammed Sabbih Hamoud Al-Tamimi, Sabine U. Peters, and Ghazali Sulong. "New Combined Technique for Fingerprint Image Enhancement." Modern Applied Science 11, no. 1 (December 19, 2016): 222. http://dx.doi.org/10.5539/mas.v11n1p222.
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This paper presents a combination of enhancement techniques for fingerprint images affected by different type of noise. These techniques were applied to improve image quality and come up with an acceptable image contrast. The proposed method included five different enhancement techniques: Normalization, Histogram Equalization, Binarization, Skeletonization and Fusion. The Normalization process standardized the pixel intensity which facilitated the processing of subsequent image enhancement stages. Subsequently, the Histogram Equalization technique increased the contrast of the images. Furthermore, the Binarization and Skeletonization techniques were implemented to differentiate between the ridge and valley structures and to obtain one pixel-wide lines. Finally, the Fusion technique was used to merge the results of the Histogram Equalization process with the Skeletonization process to obtain the new high contrast images. The proposed method was tested in different quality images from National Institute of Standard and Technology (NIST) special database 14. The experimental results are very encouraging and the current enhancement method appeared to be effective by improving different quality images.
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Nasonov, A. V., and A. S. Krylov. "Edge quality metrics for image enhancement." Pattern Recognition and Image Analysis 22, no. 2 (June 2012): 346–53. http://dx.doi.org/10.1134/s1054661812020113.
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Buzug, Thorsten M., and Jürgen Weese. "Image registration for DSA quality enhancement." Computerized Medical Imaging and Graphics 22, no. 2 (March 1998): 103–13. http://dx.doi.org/10.1016/s0895-6111(98)00012-3.
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Yao, Chen, Yan Xia, and Jiamin Zhu. "Image Enhancement by Frequency Analysis." MATEC Web of Conferences 228 (2018): 02008. http://dx.doi.org/10.1051/matecconf/201822802008.
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Because of lighting or the quality of CMOS/CCD, poor images are often gained, which greatly affect subjective observation. Image enhancement can improve the contrast of poor image. In our paper, we propose a new image enhancement algorithm based on frequency analysis. A central energy of FFT is utilized for computation of image enhancement factors. A linear mapping is used for image mapping. Finally, some experimental results are shown for illustration of our algorithm advantage.
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Singh, Sukhwinder, and Amit Grover. "Adaptive Image Quality Enhancement with Hybrid Pixel Enhancement Approach." International Journal of Computer Applications 142, no. 7 (May 17, 2016): 7–11. http://dx.doi.org/10.5120/ijca2016909858.
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Sharma, Bhubneshwar, and Jyoti Dadwal. "Infrastructures and analysis of image processing technique used for enhancement image applicaton process in electronics engineering." International Journal of Advances in Scientific Research 1, no. 10 (December 30, 2015): 356. http://dx.doi.org/10.7439/ijasr.v1i10.2459.
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Principle objective of Image enhancement is to process an image so that result is more suitable than original image for specific application. image enhancement used in Quality Control ,Problem Diagnostics, Research and Development ,Insurance Risk Assessment ,Risk Management Programme, Digital infrared thermal imaging in health care, Surveillance in security, law enforcement and defence. Various enhancement schemes are used for enhancing an image which includes gray scale manipulation, filtering and Histogram Equalization (HE), fast Fourier transform. Image enhancement is the process of making images more useful. The reasons for doing this include, Highlighting interesting detail in images, removing noise from images, making images more visually appealing, edge enhancement and increase the contrast of the image.
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Vishnu, Choundur. "Low Light Image Enhancement using Convolutional Neural Network." International Journal for Research in Applied Science and Engineering Technology 9, no. VI (June 30, 2021): 3463–72. http://dx.doi.org/10.22214/ijraset.2021.35787.
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Great quality images and pictures are remarkable for some perceptions. Nonetheless, not each and every images are in acceptable features and quality as they are capture in non-identical light atmosphere. At the point when an image is capture in a low light state the pixel esteems are in a low-esteem range, which will cause image quality to decrease evidently. Since the entire image shows up dull, it's difficult to recognize items or surfaces clearly. Thus, it is vital to improve the nature of low-light images. Low light image enhancement is required in numerous PC vision undertakings for object location and scene understanding. In some cases there is a condition when image caught in low light consistently experience the ill effects of low difference and splendor which builds the trouble of resulting undeniable level undertaking in incredible degree. Low light image improvement utilizing convolutional neural network framework accepts dull or dark images as information and creates brilliant images as a yield without upsetting the substance of the image. So understanding the scene caught through image becomes simpler task.
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Kaur, Gurjeet, and Dr Sukhwinder Singh. "Image Quality Enhancement and Noise Reduction in Kidney Ultrasound Images." International Journal for Research in Applied Science and Engineering Technology 10, no. 7 (July 31, 2022): 1204–9. http://dx.doi.org/10.22214/ijraset.2022.45490.
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Abstract: Kidney disease should not be ignored as kidney failure can endanger our lives. Hence, early detection and prevention are essential to prevent various kidney problems in patients. One of the most important medical diagnostic techniques for examining kidney stones and other kidney-related problems, is ultrasound imaging. Moreover, ultrasound imaging is efficient, radiation-free, cost-effective, and real-time. However, low contrast, speckle noise, gaussian noise, and other aberrations still limit access to ultrasonic scanning facilities. So, improved image quality is a major need for diagnosing renal problems. As a result, it is important to improve the image quality to detect kidney problems. Appropriate imaging techniques are available for preprocessing, noise reduction, and image enhancement to address these issues. An important way to get high quality images from noisy images is to improve the image and restore it. Because of the high-frequency rate, noise appears in the kidney ultrasound images. Preprocessing is the initial stage in obtaining a high-quality image. In this process, the image quality is improved. The preprocessing involves the use of various filtering techniques to filter the image and eliminate noise. The problems of speckle, salt-and-pepper and Gaussian noise in ultrasound images can be overcome using various filters like mean, medium, wiener and many more. The research work presents an overview of the various techniques for ultrasound image preprocessing and quality enhancement. Moreover, the objectives of these techniques and their performance are explained.
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Kosugi, Satoshi, and Toshihiko Yamasaki. "Unpaired Image Enhancement Featuring Reinforcement-Learning-Controlled Image Editing Software." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 07 (April 3, 2020): 11296–303. http://dx.doi.org/10.1609/aaai.v34i07.6790.
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This paper tackles unpaired image enhancement, a task of learning a mapping function which transforms input images into enhanced images in the absence of input-output image pairs. Our method is based on generative adversarial networks (GANs), but instead of simply generating images with a neural network, we enhance images utilizing image editing software such as Adobe® Photoshop® for the following three benefits: enhanced images have no artifacts, the same enhancement can be applied to larger images, and the enhancement is interpretable. To incorporate image editing software into a GAN, we propose a reinforcement learning framework where the generator works as the agent that selects the software's parameters and is rewarded when it fools the discriminator. Our framework can use high-quality non-differentiable filters present in image editing software, which enables image enhancement with high performance. We apply the proposed method to two unpaired image enhancement tasks: photo enhancement and face beautification. Our experimental results demonstrate that the proposed method achieves better performance, compared to the performances of the state-of-the-art methods based on unpaired learning.
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Correia, João, Nereida Rodriguez-Fernandez, Leonardo Vieira, Juan Romero, and Penousal Machado. "Towards Automatic Image Enhancement with Genetic Programming and Machine Learning." Applied Sciences 12, no. 4 (February 20, 2022): 2212. http://dx.doi.org/10.3390/app12042212.
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Image Enhancement (IE) is an image processing procedure in which the image’s original information is improved, highlighting specific features to ease post-processing analyses by a human or machine. State-of-the-art image enhancement pipelines apply solutions to fixed and static constraints to solve specific issues in isolation. In this work, an IE system for image marketing is proposed, more precisely, real estate marketing, where the objective is to enhance the commercial appeal of the images, while maintaining a level of realism and similarity with the original image. This work proposes a generic image enhancement pipeline that combines state-of-the-art image processing filters, Machine Learning methods, and Evolutionary approaches, such as Genetic Programming (GP), to create a dynamic framework for Image Enhancement. The GP-based system is trained to optimize 4 metrics: Neural Image Assessment (NIMA) technical and BRISQUE, which evaluate the technical quality of the images; and NIMA aesthetics and PhotoILike, that evaluate the commercial attractiveness. It is shown that the GP model was able to find the best image quality enhancement (0.97 NIMA Aesthetics), while maintaining a high level of similarity with the original images (Structural Similarity Index Measure (SSIM) of 0.88). The framework has better performance according to the image quality metrics than the off-the-shelf image enhancement tool and the framework’s isolated parts.
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Pardhasaradhi, P., B. T PMadhav, G. Lakshmi Sindhuja, K. Sai Sreeram, M. Parvathi, and B. Lokesh. "Image enhancement with contrast coefficients using wavelet based image fusion." International Journal of Engineering & Technology 7, no. 2.8 (March 19, 2018): 432. http://dx.doi.org/10.14419/ijet.v7i2.8.10476.
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The future is mainly focused on image brightness and the capacity that required storing the image. The sharp images provide better information than the blur images. To overcome from the blurriness in the image, we use image enhancement techniques. Image fusion used to overcome information loss in the image. This paper is provided with image enhancement and fusion by applying wavelet transform technique. Wavelet transform is mainly used because due to its inherent property that is they are redundant and shift invariant. It transforms the image into different scales. Image enhancement will be decided based on the levels of transformation. Low contrast results from poor resolution, lack of dynamic range, wrong settings of sensor lens during acquisition and poor quality of cameras and sensors. To avoid the information loss there is an interesting solution that is for the pictures of the same image but focused on different regions. Then using image fusion concept, all images which are captured are combined to get a single image which contains the properties of both the source images. The image entropy is composed to determine the quality of the image. The paper shows the image fusion method for both multi-resolution and images captured at different temperatures.
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Altynay, Kadyrova, Pedersen Marius, Ahmad Bilal, Mandal Dipendra J., Nguyen Mathieu, and Hardeberg Zimmermann Pauline. "Image enhancement dataset for evaluation of image quality metrics." Electronic Imaging 34, no. 9 (January 16, 2022): 317–1. http://dx.doi.org/10.2352/ei.2022.34.9.iqsp-317.
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Yun, Eun-Kyung, and Sung-Bae Cho. "Adaptive fingerprint image enhancement with fingerprint image quality analysis." Image and Vision Computing 24, no. 1 (January 2006): 101–10. http://dx.doi.org/10.1016/j.imavis.2005.09.017.
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Maksymiv, Mykola, and Taras Rak. "Methods to Increase the Contrast of the Image with Preserving the Visual Quality." Advances in Cyber-Physical Systems 6, no. 2 (December 17, 2021): 140–45. http://dx.doi.org/10.23939/acps2021.02.140.
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Contrast enhancement is a technique for increasing the contrast of an image to obtain better image quality. As many existing contrast enhancement algorithms typically add too much contrast to an image, maintaining visual quality should be considered as a part of enhancing image contrast. This paper focuses on a contrast enhancement method that is based on histogram transformations to improve contrast and uses image quality assessment to automatically select the optimal target histogram. Improvements in contrast and preservation of visual quality are taken into account in the target histogram, so this method avoids the problem of excessive increase in contrast. In the proposed method, the optimal target histogram is the weighted sum of the original histogram, homogeneous histogram and Gaussian histogram. Structural and statistical metrics of “naturalness of the image” are used to determine the weights of the corresponding histograms. Contrast images are obtained by matching the optimal target histogram. Experiments show that the proposed method gives better results compared to other existing algorithms for increasing contrast based on the transformation of histograms.
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Dwivedi, Ashish, and Nirupma Tiwari. "Analysis of color Image Enhancement Using DWT, Wavelet Shrinkageand FHE Methods." International Journal of Advanced Research in Computer Science and Software Engineering 7, no. 8 (August 30, 2017): 56. http://dx.doi.org/10.23956/ijarcsse.v7i8.21.
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Image enhancement (IE) is very important in the field where visual appearance of an image is the main. Image enhancement is the process of improving the image in such a way that the resulting or output image is more suitable than the original image for specific task. With the help of image enhancement process the quality of image can be improved to get good quality images so that they can be clear for human perception or for the further analysis done by machines.Image enhancement method enhances the quality, visual appearance, improves clarity of images, removes blurring and noise, increases contrast and reveals details. The aim of this paper is to study and determine limitations of the existing IE techniques. This paper will provide an overview of different IE techniques commonly used. We Applied DWT on original RGB image then we applied FHE (Fuzzy Histogram Equalization) after DWT we have done the wavelet shrinkage on Three bands (LH, HL, HH). After that we fuse the shrinkage image and FHE image together and we get the enhance image.
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AILISTO, HEIKKI, MIKKO LINDHOLM, and PAULI TIKKANEN. "A REVIEW OF FINGERPRINT IMAGE ENHANCEMENT METHODS." International Journal of Image and Graphics 03, no. 03 (July 2003): 401–24. http://dx.doi.org/10.1142/s0219467803001081.
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Automatic fingerprint identification methods have become the most widely used technology in rapidly growing bioidentification applications. In this paper, different image enhancement approaches presented in the scientific literature are reviewed. Fingerprint verification can be divided into image acquisition, enhancement, feature extraction and matching steps. The enhancement step is needed to improve image quality prior to feature extraction. By far the most common approach relies on the filtering of the fingerprint images with filters adapted to local ridge orientation, but alternative approaches based on Fourier domain processing, direct ridge following and global features also exist. Methods of comparing the performance of enhancement methods are discussed. An example of the performance of different methods is given. Conclusions are made regarding the importance of effective enhancement, especially for noisy or low quality images.
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Mu, Qi, Xinyue Wang, Yanyan Wei, and Zhanli Li. "Low and non-uniform illumination color image enhancement using weighted guided image filtering." Computational Visual Media 7, no. 4 (July 23, 2021): 529–46. http://dx.doi.org/10.1007/s41095-021-0232-x.
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AbstractIn the state of the art, grayscale image enhancement algorithms are typically adopted for enhancement of RGB color images captured with low or non-uniform illumination. As these methods are applied to each RGB channel independently, imbalanced inter-channel enhancements (color distortion) can often be observed in the resulting images. On the other hand, images with non-uniform illumination enhanced by the retinex algorithm are prone to artifacts such as local blurring, halos, and over-enhancement. To address these problems, an improved RGB color image enhancement method is proposed for images captured under non-uniform illumination or in poor visibility, based on weighted guided image filtering (WGIF). Unlike the conventional retinex algorithm and its variants, WGIF uses a surround function instead of a Gaussian filter to estimate the illumination component; it avoids local blurring and halo artifacts due to its anisotropy and adaptive local regularization. To limit color distortion, RGB images are first converted to HSI (hue, saturation, intensity) color space, where only the intensity channel is enhanced, before being converted back to RGB space by a linear color restoration algorithm. Experimental results show that the proposed method is effective for both RGB color and grayscale images captured under low exposure and non-uniform illumination, with better visual quality and objective evaluation scores than from comparator algorithms. It is also efficient due to use of a linear color restoration algorithm.
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Sharma, Puspad Kumar, Nitesh Gupta, and Anurag Shrivastava. "Edge Enhancement from Low-Light Image by Convolutional Neural Network and Sigmoid Function." IJOSTHE 7, no. 1 (February 10, 2020): 8. http://dx.doi.org/10.24113/ojssports.v7i1.116.
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Due to camera resolution or any lighting condition, captured image are generally over-exposed or under-exposed conditions. So, there is need of some enhancement techniques that improvise these artifacts from recorded pictures or images. So, the objective of image enhancement and adjustment techniques is to improve the quality and characteristics of an image. In general terms, the enhancement of image distorts the original numerical values of an image. Therefore, it is required to design such enhancement technique that do not compromise with the quality of the image. The optimization of the image extracts the characteristics of the image instead of restoring the degraded image. The improvement of the image involves the degraded image processing and the improvement of its visual aspect. A lot of research has been done to improve the image. Many research works have been done in this field. One among them is deep learning. Most of the existing contrast enhancement methods, adjust the tone curve to correct the contrast of an input image but doesn’t work efficiently due to limited amount of information contained in a single image. In this research, the CNN with edge adjustment is proposed. By applying CNN with Edge adjustment technique, the input low contrast images are capable to adapt according to high quality enhancement. The result analysis shows that the developed technique significantly advantages over existing methods.
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Suryaprabha, D., J. Satheeshkumar, and N. Seenivasan. "Classical and Fuzzy Based Image Enhancement Techniques for Banana Root Disease Diagnosis: A Review and Validation." Oriental journal of computer science and technology 13, no. 1 (May 19, 2020): 50–62. http://dx.doi.org/10.13005/ojcst13.01.05.
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A vital step in automation of plant root disease diagnosis is to extract root region from the input images in an automatic and consistent manner. However, performance of segmentation algorithm over root images directly depends on the quality of input images. During acquisition, the captured root images are distorted by numerous external factors like lighting conditions, dust and so on. Hence it is essential to incorporate an image enhancement algorithm as a pre-processing step in the plant root disease diagnosis module. Image quality can be improved either by manipulating the pixels through spatial or frequency domain. In spatial domain, images are directly manipulated using their pixel values and alternatively in frequency domain, images are indirectly manipulated using transformations. Spatial based enhancement methods are considered as favourable approach for real time root images as it is simple and easy to understand with low computational complexity. In this study, real time banana root images were enhanced by attempting with different spatial based image enhancement techniques. Different classical point processing methods (contrast stretching, logarithmic transformation, power law transformation, histogram equalization, adaptive histogram equalization and histogram matching) and fuzzy based enhancement methods using fuzzy intensification operator and fuzzy if-then rule based methods were tried to enhance the banana root images. Quality of the enhanced root images obtained through different classical point processing and fuzzy based methods were measured using no-reference image quality metrics, entropy and blind image quality index. Hence, this study concludes that fuzzy based method could be deployed as a suitable image enhancement algorithm while devising the image processing modules for banana root disease diagnosis.
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Albahar, Marwan Ali. "Contrast and Synthetic Multiexposure Fusion for Image Enhancement." Computational Intelligence and Neuroscience 2021 (September 3, 2021): 1–10. http://dx.doi.org/10.1155/2021/2030142.
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Many hardware and software advancements have been made to improve image quality in smartphones, but unsuitable lighting conditions are still a significant impediment to image quality. To counter this problem, we present an image enhancement pipeline comprising synthetic multi-image exposure fusion and contrast enhancement robust to different lighting conditions. In this paper, we propose a novel technique of generating synthetic multi-exposure images by applying gamma correction to an input image using different values according to its luminosity for generating multiple intermediate images, which are then transformed into a final synthetic image by applying contrast enhancement. We observed that our proposed contrast enhancement technique focuses on specific regions of an image resulting in varying exposure, colors, and details for generating synthetic images. Visual and statistical analysis shows that our method performs better in various lighting scenarios and achieves better statistical naturalness and discrete entropy scores than state-of-the-art methods.
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Wang, Zhaoyang, Dan Zhao, and Yunfeng Cao. "Image Quality Enhancement with Applications to Unmanned Aerial Vehicle Obstacle Detection." Aerospace 9, no. 12 (December 15, 2022): 829. http://dx.doi.org/10.3390/aerospace9120829.
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Aiming at the problem that obstacle avoidance of unmanned aerial vehicles (UAVs) cannot effectively detect obstacles under low illumination, this research proposes an enhancement algorithm for low-light airborne images, which is based on the camera response model and Retinex theory. Firstly, the mathematical model of low-illumination image enhancement is established, and the relationship between the camera response function (CRF) and brightness transfer function (BTF) is constructed by a common parameter equation. Secondly, to solve the problem that the enhancement algorithm using the camera response model will lead to blurred image details, Retinex theory is introduced into the camera response model to design an enhancement algorithm framework suitable for UAV obstacle avoidance. Thirdly, to shorten the time consumption of the algorithm, an acceleration solver is adopted to calculate the illumination map, and the exposure matrix is further calculated via the illumination map. Additionally, the maximum exposure value is set for low signal-to-noise ratio (SNR) pixels to suppress noise. Finally, a camera response model and exposure matrix are used to adjust the low-light image to obtain an enhanced image. The enhancement experiment for the constructed dataset shows that the proposed algorithm can significantly enhance the brightness of low-illumination images, and is superior to other similar available algorithms in quantitative evaluation metrics. Compared with the illumination enhancement algorithm based on infrared and visible image fusion, the proposed algorithm can achieve illumination enhancement without introducing additional airborne sensors. The obstacle object detection experiment shows that the proposed algorithm can increase the AP (average precision) value by 0.556.
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DZULKIFLI, FAHMI AKMAL. "Identification of Suitable Contrast Enhancement Technique for Improving the Quality of Astrocytoma Histopathological Images." ELCVIA Electronic Letters on Computer Vision and Image Analysis 20, no. 1 (May 27, 2021): 84–98. http://dx.doi.org/10.5565/rev/elcvia.1256.
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Contrast enhancement plays an important part in image processing. In histology, the application of a contrast enhancement technique is necessary since it can help pathologists in diagnosing the sample slides by increasing the visibility of the morphological and features of cells in an image. Various techniques have been proposed to enhance the contrast of microscopic images. Thus, this paper aimed to study the effectiveness of contrast enhancement techniques in enhancing the Ki67 images of astrocytoma. Three contrast enhancement techniques consist of contrast stretching, histogram equalization, and CLAHE techniques were proposed to enhance the sample images. The performance of each technique was compared by computing seven quantitative measures. The CLAHE technique was preferred for enhancing the contrast of the astrocytoma images. This technique produces good results especially in contrast enhancement, edge conservation and enhancement, brightness preservation, and minimum distortions to the enhanced images.
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Gupta, Shubhanshi, Ashutosh Gupta, and Gagan Minocha. "Image Enhancement based on Contrast Enhancement & Fuzzification Histogram Equalization and Comparison with Contrast Enhancement Techniques." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 7, no. 2 (June 5, 2013): 594–99. http://dx.doi.org/10.24297/ijct.v7i2.3461.
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Contrast Enhancement is a technique which comes into the part of Image Enhancement. Contrast Enhancement is used to enhance the visual quality of any captured or other image. Contrast Enhancement can be performed with the help of Histogram equalization (HE). In this technique, the image is collected in the gray scale allocation. The image is then partitioning and applying adaptive Histogram equalization (AHE). Fuzzy logic provides a set of logics which enhance the contrast and visibility of any image. In this technique, the visual quality and the contrast of image will change and then compare these results with previous techniques. The performance of several established image enhancement techniques is presented in terms of different parameters like Absolute mean brightness error (AMBE), Peak signal to noise ratio (PSNR), contrast and Visual quality.
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Hu, Kai, Yanwen Zhang, Chenghang Weng, Pengsheng Wang, Zhiliang Deng, and Yunping Liu. "An Underwater Image Enhancement Algorithm Based on Generative Adversarial Network and Natural Image Quality Evaluation Index." Journal of Marine Science and Engineering 9, no. 7 (June 24, 2021): 691. http://dx.doi.org/10.3390/jmse9070691.
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When underwater vehicles work, underwater images are often absorbed by light and scattered and diffused by floating objects, which leads to the degradation of underwater images. The generative adversarial network (GAN) is widely used in underwater image enhancement tasks because it can complete image-style conversions with high efficiency and high quality. Although the GAN converts low-quality underwater images into high-quality underwater images (truth images), the dataset of truth images also affects high-quality underwater images. However, an underwater truth image lacks underwater image enhancement, which leads to a poor effect of the generated image. Thus, this paper proposes to add the natural image quality evaluation (NIQE) index to the GAN to provide generated images with higher contrast and make them more in line with the perception of the human eye, and at the same time, grant generated images a better effect than the truth images set by the existing dataset. In this paper, several groups of experiments are compared, and through the subjective evaluation and objective evaluation indicators, it is verified that the enhanced image of this algorithm is better than the truth image set by the existing dataset.
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Satapathy, Lalit Mohan, and Pranati Das. "VMD Based Image Quality Enhancement Using Multi Technology Fusion." Review of Computer Engineering Research 9, no. 1 (May 9, 2022): 44–54. http://dx.doi.org/10.18488/76.v9i1.2991.
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Despite the success of various enhancement techniques used in many bio-medical applications, edge-preservation-based image enhancement remains a limiting factor for image quality and thus the usefulness of these techniques. In this paper, a new enhancement technique combining the variational mode decomposition (VMD) with the Sobel gradient and equalization technique is proposed. The proposed algorithm first decomposes the image into various sub-modes based on their frequency. The low-frequency components are equalized using the conventional equalization technique, whereas the high-frequency components use a traditional filter. Finally, the edge of the original image is added to the processed image for quality assurance. The proposed algorithm has two advantages over the existing approaches by enhancing only the low-frequency components to extract the hidden artefacts and specifically de-noising the high-frequency component. This process not only enhances the contrast, but also preserves the brightness of the image. A comprehensive study was conducted on the experimental results of benchmark test images using different performance measure matrices to quantify the effectiveness of the approach. In terms of both subjective and objective evaluation, the reconstructed image is found to be more accurate and visually pleasing. It also outperforms the state-of-the-art image-fusion methods, especially in terms of PSNR, RMSE, mutual information, and structural similarity.
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Voronin, Viacheslav. "Modified Local and Global Contrast Enhancement Algorithm for Color Satellite Image." EPJ Web of Conferences 224 (2019): 04010. http://dx.doi.org/10.1051/epjconf/201922404010.
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The quality of remotely sensed satellite images depends on the reflected electromagnetic radiation from the earth’s surface features. Lack of consistent and similar amounts of energy reflected by different features from the earth’s surface results in a poor contrast satellite image. Image enhancement is the image processing of improving the quality that the results are more suitable for display or further image analysis. In this paper, we present a detailed model for color image enhancement using the quaternion framework. We introduce a novel quaternionic frequency enhancement algorithm that can combine the color channels and the local and global image processing. The basic idea is to apply the α-rooting image enhancement approach for different image blocks. For this purpose, we split image in moving windows on disjoint blocks. The parameter alfa for every block and the weights for every local and global enhanced image driven through optimization of measure of enhancement (EMEC). Some presented experimental results illustrate the performance of the proposed approach on color satellite images in comparison with the state-of-the-art methods.
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Huang, Shih-Chia, Quoc-Viet Hoang, Trung-Hieu Le, Yan-Tsung Peng, Ching-Chun Huang, Cheng Zhang, Benjamin C. M. Fung, Kai-Han Cheng, and Sha-Wo Huang. "An Advanced Noise Reduction and Edge Enhancement Algorithm." Sensors 21, no. 16 (August 10, 2021): 5391. http://dx.doi.org/10.3390/s21165391.
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Complementary metal-oxide-semiconductor (CMOS) image sensors can cause noise in images collected or transmitted in unfavorable environments, especially low-illumination scenarios. Numerous approaches have been developed to solve the problem of image noise removal. However, producing natural and high-quality denoised images remains a crucial challenge. To meet this challenge, we introduce a novel approach for image denoising with the following three main contributions. First, we devise a deep image prior-based module that can produce a noise-reduced image as well as a contrast-enhanced denoised one from a noisy input image. Second, the produced images are passed through a proposed image fusion (IF) module based on Laplacian pyramid decomposition to combine them and prevent noise amplification and color shift. Finally, we introduce a progressive refinement (PR) module, which adopts the summed-area tables to take advantage of spatially correlated information for edge and image quality enhancement. Qualitative and quantitative evaluations demonstrate the efficiency, superiority, and robustness of our proposed method.
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Chen, Shizhao, Qian Zhou, and Hua Zou. "A Novel Un-Supervised GAN for Fundus Image Enhancement with Classification Prior Loss." Electronics 11, no. 7 (March 24, 2022): 1000. http://dx.doi.org/10.3390/electronics11071000.
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Fundus images captured for clinical diagnosis usually suffer from degradation factors due to variation in equipment, operators, or environment. These degraded fundus images need to be enhanced to achieve better diagnosis and improve the results of downstream tasks. As there is no paired low- and high-quality fundus image, existing methods mainly focus on supervised or semi-supervised learning methods for color fundus image enhancement (CFIE) tasks by utilizing synthetic image pairs. Consequently, domain gaps between real images and synthetic images arise. With respect to existing unsupervised methods, the most important low scale pathological features and structural information in degraded fundus images are prone to be erased after enhancement. To solve these problems, an unsupervised GAN is proposed for CFIE tasks utilizing adversarial training to enhance low quality fundus images. Synthetic image pairs are no longer required during the training. A specially designed U-Net with skip connection in our enhancement network can effectively remove degradation factors while preserving pathological features and structural information. Global and local discriminators adopted in the GAN lead to better illumination uniformity in the enhanced fundus image. To better improve the visual quality of enhanced fundus images, a novel non-reference loss function based on a pretrained fundus image quality classification network was designed to guide the enhancement network to produce high quality images. Experiments demonstrated that our method could effectively remove degradation factors in low-quality fundus images and produce a competitive result compared with previous methods in both quantitative and qualitative metrics.
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Murali, V., and T. Venkateswarlu. "A Novel Technique for Automatic Image Enhancement using HTHET Approach." Asian Journal of Computer Science and Technology 8, no. 1 (February 5, 2019): 26–31. http://dx.doi.org/10.51983/ajcst-2019.8.1.2123.
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Image enhancement techniques are methods used for producing images with better quality than the original image. None of the existing methods increase the information content of the image, and are usually of little interest for subsequent automatic analysis of images. In this paper, automated Image Enhancement is achieved by carrying out Histogram techniques. Histogram equalization (HE) is a spatial domain image enhancement technique, which effectively enhances the contrast of an image. We make use of Transformation and Hyperbolization techniques for automatic image enhancement. However, while it takes care of contrast enhancement, a modified histogram equalization technique, Histogram Transformation and Hyperbolization Equalization Technique (HTHET) using optimization method is proposed using EQHIST and LINHIST.
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Bhadouria, Aashi Singh. "Underwater Image Enhancement Techniques: An Exhaustive Study." International Journal for Research in Applied Science and Engineering Technology 10, no. 2 (February 28, 2022): 812–27. http://dx.doi.org/10.22214/ijraset.2022.40388.
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Abstract: This paper discusses the various different techniques and their comparative study that will help us for improving underwater image enhancement, These underwater images usually suffer from motion blur effect due to turbulence in the flow of water and non-uniform illumination and limited contrast, Due to the presence of distortion captured underwater image needs to be processed in different ways because the underwater images captured in deep low light environment are of worst quality and these images are low contrast, cause blurring effect, limited range visibility, low contrast, hazy, light transportation is very less, scattering, absorption, noise, natural light absorption, dispersion, color variations, clarity of image is reduced, absence of natural colors, quality of the image degrades and these underwater images cannot be directly used for various computer vision techniques, experiments, object tracking system, scientific research, marine biology research, underwater vehicles, detecting system, counting system, submarine operations, underwater navigation system, disaster prevention system, maintenance of oil rigs. When captured in the underwater environment as compared to images from a clearer environment, The turbid nature of water due to the presence of particles such as minerals, salt, sand, planktons is the major obstacle in the area of underwater research. These particles produce haziness in the deep underwater captured images and it is suitable for use and many other fields. Different methods made for processing of these underwater images there are different filtering techniques, enhancement techniques preprocessing and image restoration techniques are available and some of them are discussed in this paper with their results. Keywords: Underwater Image, Image Enhancement, Image Dehazing, Light Scattering, Filtering Technique, Pre-processing Technique, Haze Environment, Multi-scale wavelet.
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Liu, Tao, Hao Liu, Yingying Wu, Bo Yin, and Zhiqiang Wei. "Exposure Bracketing Techniques for Camera Document Image Enhancement." Applied Sciences 9, no. 21 (October 25, 2019): 4529. http://dx.doi.org/10.3390/app9214529.
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Capturing document images using digital cameras in uneven lighting conditions is challenging, leading to poorly captured images, which hinders the processing that follows, such as Optical Character Recognition (OCR). In this paper, we propose the use of exposure bracketing techniques to solve this problem. Instead of capturing one image, we used several images that were captured with different exposure settings and used the exposure bracketing technique to generate a high-quality image that incorporates useful information from each image. We found that this technique can enhance image quality and provides an effective way of improving OCR accuracy. Our contributions in this paper are two-fold: (1) a preprocessing chain that uses exposure bracketing techniques for document images is discussed, and an automatic registration method is proposed to find the geometric disparity between multiple document images, which lays the foundation for exposure bracketing; (2) several representative exposure bracketing algorithms are incorporated in the processing chain and their performances are evaluated and compared.
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Hu, Kai, Yanwen Zhang, Feiyu Lu, Zhiliang Deng, and Yunping Liu. "An Underwater Image Enhancement Algorithm Based on MSR Parameter Optimization." Journal of Marine Science and Engineering 8, no. 10 (September 25, 2020): 741. http://dx.doi.org/10.3390/jmse8100741.
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The quality of underwater images is often affected by the absorption of light and the scattering and diffusion of floating objects. Therefore, underwater image enhancement algorithms have been widely studied. In this area, algorithms based on Multi-Scale Retinex (MSR) represent an important research direction. Although the visual quality of underwater images can be improved to some extent, the enhancement effect is not good due to the fact that the parameters of these algorithms cannot adapt to different underwater environments. To solve this problem, based on classical MSR, we propose an underwater image enhancement optimization (MSR-PO) algorithm which uses the non-reference image quality assessment (NR-IQA) index as the optimization index. First of all, in a large number of experiments, we choose the Natural Image Quality Evaluator (NIQE) as the NR-IQA index and determine the appropriate parameters in MSR as the optimization object. Then, we use the Gravitational Search Algorithm (GSA) to optimize the underwater image enhancement algorithm based on MSR and the NIQE index. The experimental results show that this algorithm has an excellent adaptive ability to environmental changes.
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Lim, Jong-Nam, Hyung-Tae Kim, Min-Hye Kim, and Kwon Su Chon. "Enhancement of Image Quality Using Detector Filter." Journal of the Korean Society of Radiology 10, no. 6 (October 31, 2016): 451–56. http://dx.doi.org/10.7742/jksr.2016.10.6.451.
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Wang, Jiachen, Yingyun Yang, and Yan Hua. "Image quality enhancement using hybrid attention networks." IET Image Processing 16, no. 2 (November 23, 2021): 521–34. http://dx.doi.org/10.1049/ipr2.12368.
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Gasparini, Francesca. "Low-quality image enhancement using visual attention." Optical Engineering 46, no. 4 (April 1, 2007): 040502. http://dx.doi.org/10.1117/1.2721764.
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Moghaddam, Reza Farrahi, and Mohamed Cheriet. "Low quality document image modeling and enhancement." International Journal of Document Analysis and Recognition (IJDAR) 11, no. 4 (February 28, 2009): 183–201. http://dx.doi.org/10.1007/s10032-008-0076-2.
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