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Статті в журналах з теми "Image enhancement"

Автор: Grafiati

Опубліковано: 4 червня 2021

Оновлено: 25 липня 2025

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1

M, Reshma, and Priestly B. Shan. "Oretinex-DI: Pre-Processing Algorithms for Melanoma Image Enhancement." Biomedical and Pharmacology Journal 11, no. 3 (2018): 1381–87. http://dx.doi.org/10.13005/bpj/1501.

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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 c

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2

B., Mrs Rajeswari. "Night Time Image Enhancement." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 04 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem29951.

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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 lear

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3

Suryamani, Singh*1 &. Mrs. Priyanka Gaur2. "REVIEW PAPER ON UNDERWATER AND SATELLITE IMAGE ENHANCEMENT USING AUTO THRESHOLD METHOD." INTERNATIONAL JOURNAL OF ENGINEERING SCIENCES & RESEARCH TECHNOLOGY 7, no. 5 (2018): 429–34. https://doi.org/10.5281/zenodo.1247299.

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Now days applications require various kinds of images as sources of information for interpretation and inspection. Image enhancement is method of applying different alterations to an input image to make the resultant image more pleasing or to provide a better transform presentation for future automated image processing techniques. Many images like medical images, images of satellites, and even real life photographs suffer from poor sharpness and noisy effects. This is essential to enhance the contrast and remove the noise to increase picture standard. One is presenting a review on various imag

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4

Prateeshwaran, P., Dr N. Keerthana, and Dr S. Kevin Andrews. "Underwater Image Enhancement Techniques." International Journal of Research Publication and Reviews 5, no. 4 (2024): 6148–55. http://dx.doi.org/10.55248/gengpi.5.0424.1129.

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5

Mohamed Y Abdallah, Yousif, Mohamed MO Yousef, and Eltayeb W Eltayeb. "Automated Enhancement of Myocardium Images using Image Processing Methods." International Journal of Science and Research (IJSR) 10, no. 7 (2021): 557–64. https://doi.org/10.21275/sr21709185141.

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6

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 (2023): 81–86. http://dx.doi.org/10.55248/gengpi.2023.4.4.34620.

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7

Syed, Nazeeburrehman, and Ali Hussain Mohameed. "Image Resolution Enhancement Using Transform." Indonesian Journal of Electrical Engineering and Computer Science 9, no. 2 (2018): 354–56. https://doi.org/10.11591/ijeecs.v9.i2.pp354-356.

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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

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8

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 (2020): 16–20. http://dx.doi.org/10.46564/ijraet.2020.v08i03.004.

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9

Mrs., Shital G. More, and L.K.Chouthmol Prof. "IMAGE ENHANCEMENT FOR SATELLITE IMAGE." INTERNATIONAL JOURNAL OF ENGINEERING SCIENCES & RESEARCH TECHNOLOGY 5, no. 3 (2016): 401–5. https://doi.org/10.5281/zenodo.47559.

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In this work, proposing an image resolution enhancement technique which generates sharper high resolution image. The proposed technique uses DWT to decompose a low resolution image into different sub bands. Then the three high frequency sub band images have been interpolated using bicubic interpolation. The high frequency sub bands obtained by SWT of the input image are being incremented into the interpolated high frequency sub bands in order to correct the estimated coefficients. In parallel, the input image is also interpolated separately Discrete wavelet transform (DWT) is one of the recent

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10

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 (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 pro

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11

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 (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

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12

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 (2017): 50. http://dx.doi.org/10.21609/jiki.v10i1.445.

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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 Equaliza

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13

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 (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

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14

Morath, Julianne M., Cynthia A. Bielecki, Wanda L. Carlson, and Katharine R. MarcAurele. "Image Enhancement." AORN Journal 53, no. 5 (1991): 1238–47. http://dx.doi.org/10.1016/s0001-2092(07)69261-8.

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15

Beardsley, Tim. "Image Enhancement." Scientific American 270, no. 3 (1994): 14–18. http://dx.doi.org/10.1038/scientificamerican0394-14.

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16

Ibrahim, Nuha Jameel, Yossra Hussain Ali, and Tarik Ahmed Rashid. "Intelligent Image Enhancement System based on Similarity Pixels." Webology 19, no. 1 (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

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17

Abebe, Mekides Assefa, and Jon Yngve Hardeberg. "Deep Learning Approaches for Whiteboard Image Quality Enhancement." Color and Imaging Conference 2019, no. 1 (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

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18

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 (2024): 1881–94. http://dx.doi.org/10.30574/wjarr.2024.22.2.1568.

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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 oversaturati

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19

Shakira, Mhaire M. Aguirre, Fredrick S. Soriano Sean, S. Guialil Jamillah, R. Hill Gabriel, M. Mahusay Leisyl, and V. Contreras Florencio. "An enhancement of the novel cuckoo search algorithm applied in contrast enhancement of gray scale images." World Journal of Advanced Research and Reviews 22 (May 30, 2024): 1881–94. https://doi.org/10.5281/zenodo.14709980.

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Анотація:

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 oversaturati

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20

Nazeeburrehman, Syed, and Mohameed Ali Hussain. "Image Resolution Enhancement Using Transform." Indonesian Journal of Electrical Engineering and Computer Science 9, no. 2 (2018): 354. http://dx.doi.org/10.11591/ijeecs.v9.i2.pp354-356.

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Анотація:

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

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21

Attia, Salim J. "Assessment of Some Enhancement Methods of Renal X-ray Image." NeuroQuantology 18, no. 12 (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 q

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22

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 (2021): 4938–41. http://dx.doi.org/10.17762/turcomat.v12i3.2006.

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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 Convolu

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23

C., Periyasamy. "Satellite Image Enhancement Using Dual Tree Complex Wavelet Transform." Bulletin of Electrical Engineering and Informatics 6, no. 4 (2017): 334–36. https://doi.org/10.11591/eei.v6i4.861.

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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

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24

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 (2023): 1195. http://dx.doi.org/10.3390/jmse11061195.

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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

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25

Zhan-Peng Cui, Zhan-Peng Cui. "Restoration and Enhancement of Fuzzy Defect Image Based on Neural Network." 電腦學刊 34, no. 4 (2023): 001–14. http://dx.doi.org/10.53106/199115992023083404001.

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<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

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26

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 (2021): 1478–84. https://doi.org/10.11591/ijeecs.v23.i3.pp1478-1484.

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Анотація:

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 cmeans clustering (FCM) technique is utilized to enhance the medical images. T

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27

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 (2019): 151–74. http://dx.doi.org/10.4018/ijamc.2019070108.

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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

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28

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 (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

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29

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 (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

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30

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 (2023): 4445. http://dx.doi.org/10.3390/electronics12214445.

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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 enhance

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31

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.

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Анотація:

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 a

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32

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.

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Анотація:

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 variou

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33

Neha, Mehta*1 Dr. SVAV Prasad 2. "CONTRAST ENHANCEMENT WITH ENTROPY MINIMIZATION TECHNIQUE." INTERNATIONAL JOURNAL OF ENGINEERING SCIENCES & RESEARCH TECHNOLOGY 6, no. 11 (2017): 535–38. https://doi.org/10.5281/zenodo.1066232.

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Анотація:

Almost every branch of medical imaging uses the concept of digital image processing for visualizing and extracting details from the data images. Thus, quality enhancement has become more important to be performed with the help of various techniques. The enhancement of an image is one of the most acceptable methods to get the deeper knowledge of any image. There are various contrast enhancement methodologies that are being used for the partitioning of any image. This paper extends the process of contrast enhancement using histogram equalization along with minimization of entropy on the ultrasou

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34

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 (2021): 1478. http://dx.doi.org/10.11591/ijeecs.v23.i3.pp1478-1484.

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Анотація:

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

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35

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 (2024): 134. http://dx.doi.org/10.3390/computers13060134.

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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 descriptio

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J., Ravi *. K. Venkat Rao N. Kishore Chandra Dev. "IMPLEMENTATION OF IMAGE RESOLUTION ENHANCEMENT." INTERNATIONAL JOURNAL OF ENGINEERING SCIENCES & RESEARCH TECHNOLOGY 6, no. 9 (2017): 494–97. https://doi.org/10.5281/zenodo.995962.

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Анотація:

This paper represents an approach to implement image resolution enhancement i.e. Stationary wavelet decomposition and Discrete Wavelet Decomposition. An image resolution enhancement technique based on interpolation of the high frequency subband images obtained by input image and the Discrete Wavelet Transform. These two type of wavelet transforms are used in several type of applications in image processing.

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Marshall, Mathews, Chummar Riya, Sandra, and Sreetha E. S. Ms. "Literature Survey on Underwater Image Enhancement." International Journal of Trend in Scientific Research and Development 4, no. 3 (2020): 920–23. https://doi.org/10.5281/zenodo.3892773.

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Анотація:

Underwater image enhancement is a challenging task and has gained priority in recent years, as the human eye cannot clearly perceive underwater images. We introduce an effective technique to develop the images captured underwater which are degraded due to the medium scattering and absorption. Our proposed method is a single image approach that does not require specialized hardware or knowledge about the structure of the hardware or underwater conditions. We introduce a new underwater image enhancement approach based on multi scale fusion strategy in this paper. In our method, we first obtain t

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Periyasamy, C. "Satellite Image Enhancement Using Dual Tree Complex Wavelet Transform." Bulletin of Electrical Engineering and Informatics 6, no. 4 (2017): 334–36. http://dx.doi.org/10.11591/eei.v6i4.861.

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Анотація:

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

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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 (2017): 737. http://dx.doi.org/10.11591/ijeecs.v8.i3.pp737-739.

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Анотація:

<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

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Kunisetty, Sivanihimaja, and Sasikala Reddy E. "Multiscale Fusion for Underwater Image Enhancement." International Journal of Emerging Research in Engineering, Science, and Management 1, no. 2 (2022): 26–33. https://doi.org/10.58482/ijeresm.v1i2.5.

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Анотація:

In this paper, an enhancement scheme on underwater images is proposed. Underwater images undergo efforts like scattering and attenuation. Many techniques are proposed for underwater image and enhancement that largely depends on the features and characteristics of light. The characterization of light signal is complex and the underlying system that depends on light will lead to additional complexities. In the proposed scheme, image fusion is done by blending two images that are generated from a single underwater image. The first image is pre-processed by white balancing and Gamma correction. Th

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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 (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) me

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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 (2015): 209–13. http://dx.doi.org/10.2174/1874120701509010209.

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Анотація:

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

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Kanika, Kapoor, and Arora Shaveta. "COLOUR IMAGE ENHANCEMENT BASED ON HISTOGRAM EQUALIZATION." Electrical & Computer Engineering: An International Journal (ECIJ) 4, no. 3 (2015): 73–82. https://doi.org/10.5281/zenodo.3593819.

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Histogram equalization is a nonlinear technique for adjusting the contrast of an image using its histogram. It increases the brightness of a gray scale image which is different from the mean brightness of the original image. There are various types of Histogram equalization techniques like Histogram Equalization, Contrast Limited Adaptive Histogram Equalization, Brightness Preserving Bi Histogram Equalization, Dualistic Sub Image Histogram Equalization, Minimum Mean Brightness Error Bi Histogram Equalization, Recursive Mean Separate Histogram Equalization and Recursive Sub Image Histogram Equa

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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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Анотація:

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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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 (2024): 1813. http://dx.doi.org/10.3390/w16131813.

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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 meth

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Gopi, Telagamalla. "Image Enhancement Techniques Using Matlab Functions." International Journal of Science and Research (IJSR) 11, no. 7 (2022): 1706–8. http://dx.doi.org/10.21275/sr22720113950.

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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.

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Shen, Kuan, Yumei Wen, and Yufang Cai. "Efficient x-ray image enhancement algorithm using image fusion." Journal of X-Ray Science and Technology: Clinical Applications of Diagnosis and Therapeutics 17, no. 3 (2009): 207–20. http://dx.doi.org/10.3233/xst-2009-022300223.

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Multiresolution Analysis (MRA) plays an important role in image and signal processing fields, and it can extract information at different scales. Image fusion is a process of combining two or more images into an image, which extracts features from source images and provides more information than one image. The research presented in this article is aimed at the development of an automated imaging enhancement system in digital radiography (DR) images, which can clearly display all the defects in one image and don't bring blocking effect. In terms of characteristic of the collected radiographic s

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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 (2022): 558. http://dx.doi.org/10.3390/sym14030558.

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Анотація:

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.

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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 (2022): 127–37. http://dx.doi.org/10.53106/199115992022023301012.

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<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 stretche

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