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B., Mrs Rajeswari. "Night Time Image Enhancement." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 04 (April 2, 2024): 1–5. http://dx.doi.org/10.55041/ijsrem29951.
Повний текст джерелаАнотація:
Night time image enhancement plays a crucial role in various applications such as surveillance, autonomous driving, and photography. However, capturing high-quality images in low-light conditions remains challenging due to limited visibility and increased noise levels. In this project, we propose a novel approach for enhancing nighttime images using MIRNet, a state-of-the-art deep learning architecture specifically designed for low-light image enhancement tasks. We collect a dataset of low-light images paired with their corresponding well-exposed counterparts and train the MIRNet model to learn the mapping between the two modalities. The architecture of MIRNet incorporates convolutional layers with residual connections to effectively capture low-light image features and generate visually pleasing enhancements. We evaluate the performance of our approach on a diverse range of nighttime scenes and compare the results against existing methods. Our experiments demonstrate that MIRNet produces superior results in enhancing nighttime images, significantly improving visibility, reducing noise, and preserving image details. The proposed approach holds promise for real-world applications where high-quality nighttime imagery is essential for decision-making and visual analysis. Keywords: Night time image enhancement, MIRNet, Deep learning, Low-light imaging, Image Processing,Convolutional neural networks (CNNs),Residual connections, Supervised learning, Dataset preparation,Imagequalityimprovement,Noisereduction,Visibilityenhancement,Surveillance,Autonomousdriving,Photography.
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M, Reshma, and Priestly B. Shan. "Oretinex-DI: Pre-Processing Algorithms for Melanoma Image Enhancement." Biomedical and Pharmacology Journal 11, no. 3 (July 30, 2018): 1381–87. http://dx.doi.org/10.13005/bpj/1501.
Повний текст джерелаАнотація:
In Medical imaging, the dermoscopic images analysis is quite useful for the skin cancer detection. The automatic computer assisted diagnostic systems (CADS) require dermoscopic image enhancement for human perception and analysis. The traditional image enhancements methods lack the synchronization among contrast perception between human and the digital images. This paper proposes an optimized-Retinex (ORetinex) image enhancement algorithm to remove light effects, which is quite suitable for the dermoscopic image for clinical analysis for Melanoma. The value of global contrast factor (GCF) and contrast per pixel (CPP) is computed and compared with the traditional methods of image enhancements including contrast enhancement, CLAHE,Adaptive histogram equalization, Bilinear filtering and the proportion of GCF and CPP is found quite optimal as compare to these traditional methods.
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Sravani, L., N. Rama Venkat Sai, K. Noomika, M. Upendra Kumar, and K. V. Adarsh. "Image Enhancement of Underwater Images using Deep Learning Techniques." International Journal of Research Publication and Reviews 4, no. 4 (April 3, 2023): 81–86. http://dx.doi.org/10.55248/gengpi.2023.4.4.34620.
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Prateeshwaran, P., Dr N. Keerthana, and Dr S. Kevin Andrews. "Underwater Image Enhancement Techniques." International Journal of Research Publication and Reviews 5, no. 4 (April 28, 2024): 6148–55. http://dx.doi.org/10.55248/gengpi.5.0424.1129.
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Suralkar, S. R., and Seema Rajput. "Enhancement of Images Using Contrast Image Enhancement Techniques." International Journal Of Recent Advances in Engineering & Technology 08, no. 03 (March 30, 2020): 16–20. http://dx.doi.org/10.46564/ijraet.2020.v08i03.004.
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Sri Arsa, Dewa Made, Grafika Jati, Agung Santoso, Rafli Filano, Nurul Hanifah, and Muhammad Febrian Rachmadi. "COMPARISON OF IMAGE ENHANCEMENT METHODS FOR CHROMOSOME KARYOTYPE IMAGE ENHANCEMENT." Jurnal Ilmu Komputer dan Informasi 10, no. 1 (February 28, 2017): 50. http://dx.doi.org/10.21609/jiki.v10i1.445.
Повний текст джерелаАнотація:
The chromosome is a set of DNA structure that carry information about our life. The information can be obtained through Karyotyping. The process requires a clear image so the chromosome can be evaluate well. Preprocessing have to be done on chromosome images that is image enhancement. The process starts with image background removing. The image will be cleaned background color. The next step is image enhancement. This paper compares several methods for image enhancement. We evaluate some method in image enhancement like Histogram Equalization (HE), Contrast-limiting Adaptive Histogram Equalization (CLAHE), Histogram Equalization with 3D Block Matching (HE+BM3D), and basic image enhancement, unsharp masking. We examine and discuss the best method for enhancing chromosome image. Therefore, to evaluate the methods, the original image was manipulated by the addition of some noise and blur. Peak Signal-to-noise Ratio (PSNR) and Structural Similarity Index (SSIM) are used to examine method performance. The output of enhancement method will be compared with result of Professional software for karyotyping analysis named Ikaros MetasystemT M . Based on experimental results, HE+BM3D method gets a stable result on both scenario noised and blur image.
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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.
Повний текст джерелаАнотація:
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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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.
Повний текст джерелаАнотація:
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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Morath, Julianne M., Cynthia A. Bielecki, Wanda L. Carlson, and Katharine R. MarcAurele. "Image Enhancement." AORN Journal 53, no. 5 (May 1991): 1238–47. http://dx.doi.org/10.1016/s0001-2092(07)69261-8.
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Beardsley, Tim. "Image Enhancement." Scientific American 270, no. 3 (March 1994): 14–18. http://dx.doi.org/10.1038/scientificamerican0394-14.
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Shakira Mhaire M. Aguirre, Sean Fredrick S. Soriano, Jamillah S. Guialil, Gabriel R. Hill, Leisyl M. Mahusay, and Florencio V. Contreras. "An enhancement of the novel cuckoo search algorithm applied in contrast enhancement of gray scale images." World Journal of Advanced Research and Reviews 22, no. 2 (May 30, 2024): 1881–94. http://dx.doi.org/10.30574/wjarr.2024.22.2.1568.
Повний текст джерелаАнотація:
Image enhancement is a critical aspect of image processing, aimed at improving image quality for various applications. In this dynamic field, enhancing contrast in grayscale images is particularly significant across diverse domains such as autonomous driving, medical imaging, and pattern recognition. The Cuckoo Search Algorithm (CSA) has emerged as a promising optimization technique for image enhancement tasks due to its simplicity and efficacy. However, existing enhancements of CSA, notably the Novel Enhanced Cuckoo Search Algorithm, suffer from lengthy execution times, potential oversaturation in output, and challenges in convergence. This study proposes modifications to address these issues, focusing on reducing execution time, preserving image details, and improving convergence. Specifically, the modifications involve vectorization of the algorithm's existing code, fine-tuning of enhancement parameters, and replacing Levy flights with the Cauchy operator for better solution exploration. Experimental results demonstrate that the proposed modifications significantly enhance the algorithm's performance, leading to faster execution times, balanced enhancement, and improved overall performance based on the following five metrics: Fitness Value, Peak Signal-To-Noise Ratio (PSNR), Edge Detection, Entropy, and Feature Similarity Index (FSIM). The findings suggest that the Proposed Enhancement of the Novel Cuckoo Search Algorithm that employs Cauchy Operator yields superior results compared to Levy flights, making it a viable enhancement for image optimization tasks.
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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.
Повний текст джерелаАнотація:
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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Nazeeburrehman, Syed, and Mohameed Ali Hussain. "Image Resolution Enhancement Using Transform." Indonesian Journal of Electrical Engineering and Computer Science 9, no. 2 (February 1, 2018): 354. http://dx.doi.org/10.11591/ijeecs.v9.i2.pp354-356.
Повний текст джерелаАнотація:
In this project, interruption based image resolution enhancement technique using Discrete Wavelet Transform (DWT) with high-frequency sub bands obtained is proposed. Input images are decomposed by using DWT in this proposed enhancement technique. Inverse DWT is used to generate a new resolution enhanced image from the interpolation of high-frequency sub band images and the input low-resolution image. Intermediate stage has been proposed for estimating the high frequency sub bands to achieve a sharper image. It has been tested on benchmark images from public database. Peak Signal-To-Noise Ratio (PSNR) and visual results show the dominance of the proposed technique over the predictable and state-of-art image resolution enhancement techniques.
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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.
Повний текст джерелаАнотація:
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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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.
Повний текст джерелаАнотація:
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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Et. al., SatyasangramSahoo. "Classification among Image Enhancement Techniques for Computed Tomography scan by using CancerNet neural network." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12, no. 3 (April 11, 2021): 4938–41. http://dx.doi.org/10.17762/turcomat.v12i3.2006.
Повний текст джерелаАнотація:
Enhancement of cancerous images is a vital section of image preprocessing for Computed Tomography imaging classification. The combination of computer added pictures in X-ray is widely used for medical imaging. Basic enhancement techniques like Pixel wise Enhancements and Local operator based operation on computed Tomography (C.T.) scan are mainly used in preprocessing by using an artificially based model of the medical imaging. The study is focused on selecting the better among basic enhancement methods by using the cancerNet neural network structure. Whereas CancerNet is a widely used Convolutional neural Network structure for classification based study for cancerous medical image.
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Yan, Jiaquan, Yijian Wang, Haoyi Fan, Jiayan Huang, Antoni Grau, and Chuansheng Wang. "LEPF-Net: Light Enhancement Pixel Fusion Network for Underwater Image Enhancement." Journal of Marine Science and Engineering 11, no. 6 (June 8, 2023): 1195. http://dx.doi.org/10.3390/jmse11061195.
Повний текст джерелаАнотація:
Underwater images often suffer from degradation due to scattering and absorption. With the development of artificial intelligence, fully supervised learning-based models have been widely adopted to solve this problem. However, the enhancement performance is susceptible to the quality of the reference images, which is more pronounced in underwater image enhancement tasks because the ground truths are not available. In this paper, we propose a light-enhanced pixel fusion network (LEPF-Net) to solve this problem. Specifically, we first introduce a novel light enhancement block (LEB) based on the residual block (RB) and the light enhancement curve (LE-Curve) to restore the cast color of the images. The RB is adopted to learn and obtain the feature maps from an original input image, and the LE-Curve is used to renovate the color cast of the learned images. To realize the superb detail of the repaired images, which is superior to the reference images, we develop a pixel fusion subnetwork (PF-SubNet) that adopts a pixel attention mechanism (PAM) to eliminate noise from the underwater image. The PAM adapts weight allocation to different levels of a feature map, which leads to an enhancement in the visibility of severely degraded areas. The experimental results show that the proposed LEPF-Net outperforms most of the existing underwater image enhancement methods. Furthermore, among the five classic no-reference image quality assessment (NRIQA) indicators, the enhanced images obtained by LEPF-Net are of higher quality than the ground truths from the UIEB dataset.
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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.
Повний текст джерелаАнотація:
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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Mohammed, Hesham Hashim, Shatha A. Baker, and Omar Ibrahim Alsaif. "An Improved Underwater Image Enhancement Approach for Border Security." Journal of Image and Graphics 12, no. 2 (2024): 199–204. http://dx.doi.org/10.18178/joig.12.2.199-204.
Повний текст джерелаАнотація:
Protecting maritime borders is crucial to ensuring overall border security. Law enforcement agencies make great use of analyzing images of underwater debris to gather intelligence and detect illicit materials. Underwater image improvement contributes to better data quality and analytical. Nevertheless, underwater image analysis poses greater challenges compared to analyzing images taken above the water, factors like refraction of light and darkness contribute to the degradation of underwater image quality. In this paper, a novel approach is proposed to enhance underwater images, the proposed approach involves splitting underwater colored image to its three basic components, Subsequently, a point spread function is created for each component to describes image blurring factor, The deblurring process is then applied by using wiener filter, the result sharped by sharping filter to clarify edges, contrast linear stretch is performed to improve contrast and visual details. and the resulting image is finally reassembled from the three basic components. The proposed method showed effective results in evaluating the main metrics and gave better results when compared to a number of different methods. These results prove the effectiveness of the proposed method and its ability to practical applications in improving image quality.
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Huang, Wei, Kaili Li, Mengfan Xu, and Rui Huang. "Self-Supervised Non-Uniform Low-Light Image Enhancement Combining Image Inversion and Exposure Fusion." Electronics 12, no. 21 (October 29, 2023): 4445. http://dx.doi.org/10.3390/electronics12214445.
Повний текст джерелаАнотація:
Low-light image enhancement is a challenging task in non-uniform low-light conditions, often resulting in local overexposure, noise amplification, and color distortion. To obtain satisfactory enhancement results, most models must resort to carefully selected paired or multi-exposure data sets. In this paper, we propose a self-supervised framework for non-uniform low-light image enhancement to address these issues, only requiring low-light images on their own for training. We first design a robust Retinex model-based image exposure enhancement network (EENet) to obtain global brightness enhancement and noise removal of images by carefully designing the loss function of each decomposition map. Then, to correct overexposed areas in the enhanced image, we incorporate the inverse image of the low-light image for enhancement using EENet. Furthermore, a three-branch asymmetric exposure fusion network (TAFNet) is designed. The two enhanced images and the original image are used as the TAFNet inputs to obtain a globally well-exposed and detail-rich image. Experimental results demonstrate that our framework outperforms some state-of-the-art methods in visual and quantitative comparisons.
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Zhan-Peng Cui, Zhan-Peng Cui. "Restoration and Enhancement of Fuzzy Defect Image Based on Neural Network." 電腦學刊 34, no. 4 (August 2023): 001–14. http://dx.doi.org/10.53106/199115992023083404001.
Повний текст джерелаАнотація:
<p>In contrast enhancement of fuzzy defect image, details loss and noise expansion are east to occur, which brings difficulties to subsequent image analysis and defect recognition. Therefore, a fuzzy defect image restoration and enhancement method based on neural network is proposed. A double fusion neural network composed of a depth generation network and a discrimination network is designed. The residual of the denoised fuzzy image and the real image is output by the network, which is input into the discrimination network together with the real image, and the difference between the two is judged by the total loss function. To solve the problem of pixel coordinate value of fuzzy defect image, neural network is used to build a fast correction algorithm. Therefore, a fuzzy image restoration and enhancement method based on neural network is proposed to improve the image quality. By reconstructing the resolution of fuzzy defect image, a hierarchical enhancement method of fuzzy defect image region is constructed to achieve fuzzy defect image restoration and enhancement. The results show that the proposed method has high image processing ability in restoration and enhancement of fuzzy defect images. The fitting value of neural network is 0.92, which is significantly higher than that of the other two methods, indicating that the image restoration and enhancement method based on neural network has higher accuracy. Therefore, the restoration and enhancement method of fuzzy defect image based on neural network has a good restoration and enhancement effect, and can effectively meet the actual needs of people for high-quality images.</p> <p> </p>
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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.
Повний текст джерелаАнотація:
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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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.
Повний текст джерелаАнотація:
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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Maurya, Lalit, Prasant Kumar Mahapatra, and Amod Kumar. "A Fusion of Cuckoo Search and Multiscale Adaptive Smoothing Based Unsharp Masking for Image Enhancement." International Journal of Applied Metaheuristic Computing 10, no. 3 (July 2019): 151–74. http://dx.doi.org/10.4018/ijamc.2019070108.
Повний текст джерелаАнотація:
Image enhancement means to improve the visual appearance of an image by increasing its contrast and sharpening the features. This article presents a fusion of cuckoo search optimization-based image enhancement (CS-IE) and multiscale adaptive smoothing based unsharping method (MAS-UM) for image enhancement. The fusion strategy is introduced to improve the deficiency of enhanced image that suppresses the saturation and over-sharpness artefacts in order to obtain a visually pleasing result. The ideology behind the selection of fusion images (candidate) is that one image should have high sharpness or contrast with maximum entropy and other should be high Peak Signal-to-Noise Ratio (PSNR) sharp image, to provide a better trade-off between sharpness and noise. In this article, the CS-IE and MAS-UM results are fused to combine their complementary advantages. The proposed algorithms are applied to lathe tool images and some natural standard images to verify their effectiveness. The results are compared with conventional enhancement techniques such as Histogram equalization (HE), Linear contrast stretching (LCS), Contrast-limited adaptive histogram equalization (CLAHE), standard PSO image enhancement (PSO-IE), Differential evolution image enhancement (DE-IE) and Firefly algorithm-based image enhancement (FA-IE) techniques.
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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.
Повний текст джерелаАнотація:
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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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.
Повний текст джерелаАнотація:
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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Mohanalakshmi, K., Asha Sreenath, Telukuntla Saikeerthi, Perpula Sahana, and Mohammad Abdul Khareem. "Underwater Image Enhancement Using Wavelet Fusion." International Journal for Research in Applied Science and Engineering Technology 10, no. 11 (November 30, 2022): 1632–35. http://dx.doi.org/10.22214/ijraset.2022.47693.
Повний текст джерелаАнотація:
Abstract: The underwater images are a good source of information which explores idea about sea creatures, to study about seafloor hydrother-mal vents. Low contrast, color distortion and poor visual appearance are the major issues that an underwater image has to undergo. Such problems were caused by dispersion and refraction of light as they penetrate from rarer to denser media. The scattering of light reduces color contrast. The influence of water in underwater images is not only due to scattering but also due to the presence of underwater organisms. Here we introduce an improved method for underwater image enhancement based on the fusion method that is capable to restore accurately underwater images. The proposed work takes a single image as the input and a sequence of operations such as white balancing, gamma correction and sharpening are performed on the input image. Finally wavelet image fusion of the inputs is done to obtain the resultant output. In the initial stage, color distorted input image is white balanced to remove the color casts maintaining a realistic subsea image. In the second stage, CLAHE is performed on the gamma corrected image. CLAHE plays a significant role in luminance enhancement of underwater images. At the same time, histogram equalization is performed on the sharpened image and finally performed the wavelet fusion.
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Wang, Hua, Jianzhong Cao, Lei Yang, and Jijiang Huang. "DCTE-LLIE: A Dual Color-and-Texture-Enhancement-Based Method for Low-Light Image Enhancement." Computers 13, no. 6 (May 27, 2024): 134. http://dx.doi.org/10.3390/computers13060134.
Повний текст джерелаАнотація:
The enhancement of images captured under low-light conditions plays a vitally important role in the area of image processing and can significantly affect the performance of following operations. In recent years, deep learning techniques have been leveraged in the area of low-light image enhancement tasks, and deep-learning-based low-light image enhancement methods have been the mainstream for low-light image enhancement tasks. However, due to the inability of existing methods to effectively maintain the color distribution of the original input image and to effectively handle feature descriptions at different scales, the final enhanced image exhibits color distortion and local blurring phenomena. So, in this paper, a novel dual color-and-texture-enhancement-based low-light image enhancement method is proposed, which can effectively enhance low-light images. Firstly, a novel color enhancement block is leveraged to help maintain color distribution during the enhancement process, which can further eliminate the color distortion effect; after that, an attention-based multiscale texture enhancement block is proposed to help the network focus on multiscale local regions and extract more reliable texture representations automatically, and a fusion strategy is leveraged to fuse the multiscale feature representations automatically and finally generate the enhanced reflection component. The experimental results on public datasets and real-world low-light images established the effectiveness of the proposed method on low-light image enhancement tasks.
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Fadil, Yousra Ahmed, Baidaa Al-Bander, and Hussein Y. Radhi. "Enhancement of medical images using fuzzy logic." Indonesian Journal of Electrical Engineering and Computer Science 23, no. 3 (September 1, 2021): 1478. http://dx.doi.org/10.11591/ijeecs.v23.i3.pp1478-1484.
Повний текст джерелаАнотація:
Image enhancement is one of the most critical subjects in computer vision and image processing fields. It can be considered as means to enrich the perception of images for human viewers. All kinds of images typically suffer from different problems such as weak contrast and noise. The primary purpose of image enhancement is to change an image's visual appearance. Many algorithms have recently been proposed for enhancing medical images. Image enhancement is still deemed a challenging task. In this paper, the fuzzy c-means clustering (FCM) technique is utilized to enhance the medical images. The method of enhancement consists of two stages. The proposed algorithm conducts a cluster test on the image pixels. It then increases the difference of gray level between the diverse objects to accomplish the enhancement purpose of the medical images. The experimental results have been tested using various images. The algorithm enhanced the small target of the image to a reasonable limit and revealed favorable performance. The results of image enhancement techniques were evaluated by using terms of different criteria such as peak signal to noise ratio (PSNR), mean square error (MSE) and average information contents (AIC), showing promising performance.
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Dong, Yu Bing, Ming Jing Li, and Ying Sun. "Analysis and Comparison of Image Enhancement Methods." Applied Mechanics and Materials 713-715 (January 2015): 1593–96. http://dx.doi.org/10.4028/www.scientific.net/amm.713-715.1593.
Повний текст джерелаАнотація:
Based on the principle of the image enhancement, various image enhancement methods are introduced, analyzed and studied. Because image enhancement is closely related to the property of the interested target, the habit of observers and the specific processing goal, the image enhancement is only aimed at the given process goal, too. According to different images, these image enhancement methods are simulated by the MATLAB tools. Through comparing the test results, the results show that different methods will give different effects. Without a common image enhancement method is suitable for various occasions.
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Zhang, Hong, Ran He, and Wei Fang. "An Underwater Image Enhancement Method Based on Diffusion Model Using Dual-Layer Attention Mechanism." Water 16, no. 13 (June 26, 2024): 1813. http://dx.doi.org/10.3390/w16131813.
Повний текст джерелаАнотація:
Diffusion models have been increasingly utilized in various image-processing tasks, such as segmentation, denoising, and enhancement. These models also show exceptional performance in enhancing underwater images. However, conventional models for underwater image enhancement often face the challenge of simultaneously improving color restoration and super-resolution. This paper introduces a dual-layer attention mechanism that integrates spatial and channel attention to enhance color restoration, while preserving critical image features. Additionally, specific scale factors and interpolation methods are employed during the upsampling process to increase resolution. The proposed DL-UW method achieves significant enhancements in color, illumination, and resolution for low-quality underwater images, resulting in high PSNR, SSIM, and UIQM values. The model demonstrates a robust performance on different datasets, confirming its broad applicability and effectiveness.
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Yuan-Bin Wang, Yuan-Bin Wang, Qian Han Yuan-Bin Wang, Yu-Jie Li Qian Han, and Yuan-Yuan Li Yu-Jie Li. "Low illumination Image Enhancement based on Improved Retinex Algorithm." 電腦學刊 33, no. 1 (February 2022): 127–37. http://dx.doi.org/10.53106/199115992022023301012.
Повний текст джерелаАнотація:
<p>Aiming at the problems of insufficient illumination and low contrast of low illumination image, an improved Retinex low illumination image enhancement algorithm is proposed. Firstly, the brightness component V of the original image is extracted in HSV color space, and its enhancement by Single-Scale Retinex (SSR) is used to obtain the reflection component. For the edge problem caused by the estimation of illumination component, the Gaussian weighted bilateral filter is used as the filter function to maintain the edge information. Then, the saturation component S is adaptively stretched to improve the color saturation. However, different low illumination images have different contrast, and some images have insufficient contrast enhancement, so a global adaptive algorithm is introduced to modify the contrast and obtain the final image. According to the logarithmic characteristics of human vision, it can adaptively enhance the contrast of different images without over enhancement. Experimental results show that the proposed algorithm can effectively improve the visual quality of the image, the contrast is improved significantly and image edge details are protected, and objective evaluations such as average gradient, information entropy and peak signal-to-noise ratio have been improved.</p> <p> </p>
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Periyasamy, C. "Satellite Image Enhancement Using Dual Tree Complex Wavelet Transform." Bulletin of Electrical Engineering and Informatics 6, no. 4 (December 1, 2017): 334–36. http://dx.doi.org/10.11591/eei.v6i4.861.
Повний текст джерелаАнотація:
Drawback of losing high frequency components suffers the resolution enhancement. In this project, wavelet domain based image resolution enhancement technique using Dual Tree Complex Wavelet Transform (DT-CWT) is proposed for resolution enhancement of the satellite images. Input images are decomposed by using DT-CWT in this proposed enhancement technique. Inverse DT-CWT is used to generate a new resolution enhanced image from the interpolation of high-frequency sub band images and the input low-resolution image. Intermediate stage has been proposed for estimating the high frequency sub bands to achieve a sharper image. It has been tested on benchmark images from public database. Peak Signal-To-Noise Ratio (PSNR) and visual results show the dominance of the proposed technique over the predictable and state-of-art image resolution enhancement techniques.
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Periyasamy, C. "Satellite Image Enhancement Using Dual Tree M-Band Wavelet Transform." Indonesian Journal of Electrical Engineering and Computer Science 8, no. 3 (December 1, 2017): 737. http://dx.doi.org/10.11591/ijeecs.v8.i3.pp737-739.
Повний текст джерелаАнотація:
<p>Drawback of losing high frequency components suffers the resolution enhancement. In this project, wavelet domain based image resolution enhancement technique using Dual Tree M-Band Wavelet Transform (DTMBWT) is proposed for resolution enhancement of the satellite images. Input images are decomposed by using DTMBWT in this proposed enhancement technique. Inverse DTMBWT is used to generate a new resolution enhanced image from the interpolation of high-frequency sub band images and the input low-resolution image. Intermediate stage has been proposed for estimating the high frequency sub bands to achieve a sharper image. It has been tested on benchmark images from public database. Peak Signal-To-Noise Ratio (PSNR) and visual results show the dominance of the proposed technique over the predictable and state-of-art image resolution enhancement techniques.</p>
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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.
Повний текст джерелаАнотація:
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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Ainul Kamilah Mohd Yusoff, Rafikha Aliana A Raof, Norfadila Mahrom, Siti Suraya Md Noor, Fazrul Faiz Zakaria, and Phak Len. "Enhancement and Segmentation of Ziehl Neelson Sputum Slide Images using Contrast Enhancement and Otsu Threshold Technique." Advanced Research in Applied Sciences and Engineering Technology 30, no. 1 (March 8, 2023): 282–89. http://dx.doi.org/10.37934/araset.30.1.282289.
Повний текст джерелаАнотація:
Image processing is the most effective method for enhancement and segmentation of tuberculosis bacilli in sputum smear samples. Improper straining can result in poor screening results such as over-staining, under-staining, and blurred images. The goal is to find an image enhancement and segmentation technique that will prepare the image for feature extraction. There are still some shortcomings with existing method when it is implemented on Ziehl Neelsen images. In normal images, TB bacilli can be identified easily, but in blur and images with dark background, TB bacilli are sometimes hidden behind the sputum cells. Hence, the basic method of contrast enhancement is not enough to improve the contrast of TB bacilli as the object of interest within the image. In this study, the combination of local and partial contrast enhancement is proposed as the best method for image enhancement. Image segmentation can be accomplished using Otsu thresholding technique. Otsu's method is presented as most suitable image processing techniques in this paper. The goal of the Otsu Threshold is to find a threshold value that distinguishes the object of interest from the background. Experiment shows that the combination of local and partial contrast enhancement followed by Otsu’s method achieve an average segmentation accuracy of 98.93% when applied on 50 images of sputum smear.
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Goni, Ibrahim, Yusuf Musa Malgwi, and Asabe Sandra Ahmadu. "Satellite Image Enhancement Using Histogram Equalization." Electrical Science & Engineering 5, no. 1 (May 10, 2023): 9–20. http://dx.doi.org/10.30564/ese.v5i1.5234.
Повний текст джерелаАнотація:
Image enhancement is an indispensable technique in improving the quality, brightness, contrast and clarity of satellite images. The object that appears in images and variation caused by shadow, occlusion, camouflage in satellite images are the fundamental challenges posed by image enhancement techniques. The aim of this research work was to enhance satellite images of Sambisa using histogram equalization technique. MATLAB 2021 was used to implement the experiment. The results show that histogram equalization method has an excellent processing effect and it improved the brightness, contrast and clarity of the images as compared original images and the enhanced images.
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Siddiqi, Muhammad Hameed, and Amjad Alsirhani. "An Ensembled Spatial Enhancement Method for Image Enhancement in Healthcare." Journal of Healthcare Engineering 2022 (January 4, 2022): 1–12. http://dx.doi.org/10.1155/2022/9660820.
Повний текст джерелаАнотація:
Most medical images are low in contrast because adequate details that may prove vital decisions are not visible to the naked eye. Also, due to the low-contrast nature of the image, it is not easily segmented because there is no significant change between the pixel values, which makes the gradient very small Hence, the contour cannot converge on the edges of the object. In this work, we have proposed an ensembled spatial method for image enhancement. In this ensembled approach, we first employed the Laplacian filter, which highlights the areas of fast intensity variation. This filter can determine the sufficient details of an image. The Laplacian filter will also improve those features having shrill disjointedness. Then, the gradient of the image has been determined, which utilizes the surrounding pixels for the weighted convolution operation for noise diminishing. However, in the gradient filter, there is one negative integer in the weighting. The intensity value of the middle pixel might be deducted from the surrounding pixels, to enlarge the difference between the head-to-head pixels for calculating the gradients. This is one of the reasons due to which the gradient filter is not entirely optimistic, which may be calculated in eight directions. Therefore, the averaging filter has been utilized, which is an effective filter for image enhancement. This approach does not rely on the values that are completely diverse from distinctive values in the surrounding due to which it recollects the details of the image. The proposed approach significantly showed the best performance on various images collected in dynamic environments.
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GuruKesavaDasu, Dr Gopisetty. "Local Adaptive Image Equalization." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 04 (April 3, 2024): 1–5. http://dx.doi.org/10.55041/ijsrem29906.
Повний текст джерелаАнотація:
This paper presents a comprehensive approach to image enhancement, targeting the enhancement of contrast and reduction of noise in digital images. Leveraging state-of-the-art algorithms, the proposed methodology encompasses a strategic pipeline. Initially, the images undergo Histogram Equalization, a fundamental technique, to globally enhance contrast. Building upon this foundation, Contrast Limited Adaptive Histogram Equalization (CLAHE) is applied to achieve localized contrast enhancement, ensuring optimal balance and preservation of image details. Furthermore, the Adaptive Gamma Correction with Weighting Distribution (AGCWD) algorithm is integrated to fine-tune the enhanced images, dynamically adjusting gamma values to suppress noise and amplify visual features. The implementation harnesses Python with OpenCV and Flask frameworks, facilitating seamless integration and accessibility. Through rigorous experimentation and comparative analysis, the efficacy of the proposed approach is demonstrated, showcasing substantial improvements in image quality and fidelity. The findings underscore the practical utility and efficacy of the proposed image enhancement system, positioning it as a valuable tool for various real-world applications in image processing and computer vision domains. Keywords: Image Enhancement ,Noise Reduction ,Histogram Equalization, CLAHE, Adaptive Gamma Correction, OpenCV Library, Flask Web Framework , Image Fidelity.
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Aijing, Luo, and Yin Jin. "Research on an Improved Medical Image Enhancement Algorithm Based on P-M Model." Open Biomedical Engineering Journal 9, no. 1 (August 31, 2015): 209–13. http://dx.doi.org/10.2174/1874120701509010209.
Повний текст джерелаАнотація:
Image enhancement can improve the detail of the image to achieve the purpose of the identification of the image. At present, the image enhancement is widely used in medical images, which can help doctor’s diagnosis. IEABPM (Image Enhancement Algorithm Based on P-M Model) is one of the most common image enhancement algorithms. However, it may cause the loss of the texture details and other features. To solve the problems, this paper proposes an IIEABPM (Improved Image Enhancement Algorithm Based on P-M Model). The simulation demonstrates that IIEABPM can effectively solve the problems of IEABPM, and improve image clarity, image contrast, and image brightness.
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Sato, Tomoya. "TXI: Texture and Color Enhancement Imaging for Endoscopic Image Enhancement." Journal of Healthcare Engineering 2021 (April 7, 2021): 1–11. http://dx.doi.org/10.1155/2021/5518948.
Повний текст джерелаАнотація:
Recognition of lesions with subtle morphological and/or color changes during white light imaging (WLI) endoscopy remains a challenge. Often the endoscopic image suffers from nonuniform illumination across the image due to curvature in the lumen and the direction of the illumination light of the endoscope. We propose an image enhancement technology to resolve the drawbacks above called texture and color enhancement imaging (TXI). TXI is designed to enhance three image factors in WLI (texture, brightness, and color) in order to clearly define subtle tissue differences. In our proposed method, retinex-based enhancement is employed in the chain of endoscopic image processing. Retinex-based enhancement is combined with color enhancement to greatly accentuate color tone differences of mucosal surfaces. We apply TXI to animal endoscopic images and evaluate the performance of TXI compared with conventional endoscopic enhancement technologies, conventionally used techniques for real-world image processing, and newly proposed techniques for surgical endoscopic image augmentation. Our experimental results show that TXI can enhance brightness selectively in dark areas of an endoscopic image and can enhance subtle tissue differences such as slight morphological or color changes while simultaneously preventing over-enhancement. These experimental results demonstrate the potential of the proposed TXI algorithm as a future clinical tool for detecting gastrointestinal lesions having difficult-to-recognize tissue differences.
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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.
Повний текст джерелаАнотація:
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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Hughes-Freeland, Felicia. "Indonesian Image Enhancement." Anthropology Today 5, no. 6 (December 1989): 3. http://dx.doi.org/10.2307/3033075.
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Aghagolzadeh, Sabzali. "Transform image enhancement." Optical Engineering 31, no. 3 (1992): 614. http://dx.doi.org/10.1117/12.56095.
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Qi, Wei, Jing Han, Yi Zhang, and Lian-fa Bai. "Hierarchical image enhancement." Infrared Physics & Technology 76 (May 2016): 704–9. http://dx.doi.org/10.1016/j.infrared.2016.04.010.
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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.
Повний текст джерелаАнотація:
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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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.
Повний текст джерелаАнотація:
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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Salman, Hasan Ahmed, and Ali Kalakech. "Image Enhancement using Convolution Neural Networks." Babylonian Journal of Machine Learning 2024 (January 25, 2024): 30–47. http://dx.doi.org/10.58496/bjml/2024/003.
Повний текст джерелаАнотація:
The research presents a comprehensive exploration of the topic of image enhancement using convolutional neural networks (CNN).The research goes deeper into the advanced field of image processing based on the use of neural networks to automatically and efficiently improve the quality and detail of images. The thesis shows that convolutional neural networks are one of the types of deep neural networks, which are specially designed to gain knowledge from big data and extract complex features and patterns found in images. The different layers of the grid are discussed in detail, dealing with images incrementally and extracting different attributes in each layer. The research also highlights CNN's ability to detect, learn and improve important details found in images through convolutions, filtering and data aggregation processes. The proposed CNN image enhancement model was developed and tested on both medical and normal images. The images were optimized using the proposed model and compared with other models. Various quality measures were used to evaluate the results. The results showed that the proposed model can significantly improve the quality of images.
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Jitendra Prakash Patil, Et al. "Investigation of Image Enhancement Techniques for Advancing Colon Cancer Diagnosis." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 10 (November 2, 2023): 507–15. http://dx.doi.org/10.17762/ijritcc.v11i10.8515.
Повний текст джерелаАнотація:
Colorectal cancer continues to pose a substantial worldwide health challenge, necessitating the development of advanced imaging techniques for early and accurate diagnosis. In this study, we propose a novel hybrid image enhancement approach that combines Total Variation (TV) regularization and shift-invariant filtering to improve the visibility and diagnostic quality of colon cancer images.
The Total Variation regularization technique is employed to effectively reduce noise and enhance the edges in the input images, thereby preserving important structural details. Simultaneously, shift-invariant filtering is utilized to address spatial variations and artifacts that often arise in medical images, ensuring consistent and reliable enhancements across the entire image. Our hybrid approach synergistically integrates the strengths of both TV regularization and shift-invariant filtering, resulting in enhanced colon cancer images that offer improved contrast, reduced noise, and enhanced fine structures. This improved image quality aids medical professionals in better identifying and characterizing cancerous lesions, ultimately leading to more accurate and timely diagnoses.
To evaluate the effectiveness of the proposed approach, we conducted extensive experimentations on a diverse dataset of colon cancer images. Quantitative and qualitative assessments demonstrate that our hybrid approach outperforms existing enhancement methods, leading to superior image quality and diagnostic accuracy. In conclusion, the hybrid image enhancement approach presented in this study offers a promising solution for enhancing colon cancer images, contributing to the early detection and effective management of this life-threatening disease. These advancements hold significant potential for improving patient outcomes and reducing the burden of colon cancer on healthcare systems worldwide.
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Montazeri, Mitra. "Memetic Algorithm Image Enhancement for Preserving Mean Brightness Without Losing Image Features." International Journal of Image and Graphics 19, no. 04 (October 2019): 1950020. http://dx.doi.org/10.1142/s0219467819500207.
Повний текст джерелаАнотація:
In the image processing application, contrast enhancement is a major step. Conventional contrast enhancement methods such as Histogram Equalization (HE) do not have satisfactory results on many different low contrast images and they also cannot automatically handle different images. These problems result in specifying parameters manually to produce high contrast images. In this paper, an automatic image contrast enhancement on Memetic algorithm (MA) is proposed. In this study, simple exploiter is proposed to improve the current image contrast. The proposed method accomplishes multi goals of preserving brightness, retaining the shape features of the original histogram and controlling excessive enhancement rate, suiting for applications of consumer electronics. Simulation results shows that in terms of visual assessment, peak signal-to-noise (PSNR) and Absolute Mean Brightness Error (AMBE) the proposed method is better than the literature methods. It improves natural looking images specifically in images with high dynamic range and the output images were applicable for products of consumer electronic.