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Journal articles on the topic 'Color denoising'

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Author: Grafiati

Published: 14 March 2023

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1

Netravali, Ilka A., Robert J. Holt, and Charles Webb. "Perceptual denoising of color images." International Journal of Imaging Systems and Technology 20, no. 3 (August 16, 2010): 215–22. http://dx.doi.org/10.1002/ima.20240.

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2

Komatsu, Rina, and Tad Gonsalves. "Comparing U-Net Based Models for Denoising Color Images." AI 1, no. 4 (October 12, 2020): 465–87. http://dx.doi.org/10.3390/ai1040029.

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Digital images often become corrupted by undesirable noise during the process of acquisition, compression, storage, and transmission. Although the kinds of digital noise are varied, current denoising studies focus on denoising only a single and specific kind of noise using a devoted deep-learning model. Lack of generalization is a major limitation of these models. They cannot be extended to filter image noises other than those for which they are designed. This study deals with the design and training of a generalized deep learning denoising model that can remove five different kinds of noise from any digital image: Gaussian noise, salt-and-pepper noise, clipped whites, clipped blacks, and camera shake. The denoising model is constructed on the standard segmentation U-Net architecture and has three variants—U-Net with Group Normalization, Residual U-Net, and Dense U-Net. The combination of adversarial and L1 norm loss function re-produces sharply denoised images and show performance improvement over the standard U-Net, Denoising Convolutional Neural Network (DnCNN), and Wide Interface Network (WIN5RB) denoising models.

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3

Thomas, Jency, and Remya S. "PLOW Filter for Color Image Denoising." International Journal of Computer Applications 79, no. 13 (October 18, 2013): 1–7. http://dx.doi.org/10.5120/13798-1855.

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4

Shen, Yi, Bin Han, and Elena Braverman. "Adaptive frame-based color image denoising." Applied and Computational Harmonic Analysis 41, no. 1 (July 2016): 54–74. http://dx.doi.org/10.1016/j.acha.2015.04.001.

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5

Lukac, Rastislav, Konstantinos N. Plataniotis, and Anastasios N. Venetsanopoulos. "Color image denoising using evolutionary computation." International Journal of Imaging Systems and Technology 15, no. 5 (2005): 236–51. http://dx.doi.org/10.1002/ima.20058.

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6

He, Shui Ming, and Xue Lin Li. "Applications of Color Morphology in Image Denoising." Advanced Materials Research 1037 (October 2014): 393–97. http://dx.doi.org/10.4028/www.scientific.net/amr.1037.393.

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Mathematical morphology can be seen as a special digital image processing method and theory, which has been widely used in various fields. In this paper, the mathematical morphology is applied to the color image processing. In thespace of color image, I have simply expounded the theories and properties of color morphological changes, and defined its morphological operators. According to the application of omni-directional and multi-angle structuring elements composite morphological filter in gray image, I put forward a kind of color morphological filter with omni-directional and multi-angle structuring elements composite. This algorithm has retained its advantages in gray image, however, remaining some drawbacks. Through the optimization of results based on this algorithm, we finally get the relatively ideal denoising effects.Keywords: mathematical morphology;color model;color model; color morphological filter

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7

Liang, Dong Tai. "Color Image Denoising Using Gaussian Multiscale Multivariate Image Analysis." Applied Mechanics and Materials 37-38 (November 2010): 248–52. http://dx.doi.org/10.4028/www.scientific.net/amm.37-38.248.

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Inspired by the human vision system, a new image representation and analysis model based on Gaussian multiscale multivariate image analysis (MIA) is proposed. The multiscale color texture representations for the original image are used to constitute the multivariate image, each channel of which represents a perceptual observation from different scales. Then the MIA decomposes this multivariate image into multiscale color texture perceptual features (the principal component score images). These score images could be interpreted as 1) the output of three color opponent channels: black versus white, red versus green and blue versus yellow, and 2) the edge information, and 3) higher-order Gaussian derivatives. Finally the color image denoising approach based on the models is presented. Experiments show that this denoising method against Gaussian filters significantly improves the denoising effect by preserving more edge information.

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8

Park, Yunjin, Sukho Lee, Byeongseon Jeong, and Jungho Yoon. "Joint Demosaicing and Denoising Based on a Variational Deep Image Prior Neural Network." Sensors 20, no. 10 (May 24, 2020): 2970. http://dx.doi.org/10.3390/s20102970.

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A joint demosaicing and denoising task refers to the task of simultaneously reconstructing and denoising a color image from a patterned image obtained by a monochrome image sensor with a color filter array. Recently, inspired by the success of deep learning in many image processing tasks, there has been research to apply convolutional neural networks (CNNs) to the task of joint demosaicing and denoising. However, such CNNs need many training data to be trained, and work well only for patterned images which have the same amount of noise they have been trained on. In this paper, we propose a variational deep image prior network for joint demosaicing and denoising which can be trained on a single patterned image and works for patterned images with different levels of noise. We also propose a new RGB color filter array (CFA) which works better with the proposed network than the conventional Bayer CFA. Mathematical justifications of why the variational deep image prior network suits the task of joint demosaicing and denoising are also given, and experimental results verify the performance of the proposed method.

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9

Han, Zhenghao, Li Li, Weiqi Jin, Xia Wang, Gangcheng Jiao, Xuan Liu, and Hailin Wang. "Denoising and Motion Artifact Removal Using Deformable Kernel Prediction Neural Network for Color-Intensified CMOS." Sensors 21, no. 11 (June 4, 2021): 3891. http://dx.doi.org/10.3390/s21113891.

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Image intensifiers are used internationally as advanced military night-vision devices. They have better imaging performance in low-light-level conditions than CMOS/CCD. The intensified CMOS (ICMOS) was developed to satisfy the digital demand of image intensifiers. In order to make the ICMOS capable of color imaging in low-light-level conditions, a liquid-crystal tunable filter based color imaging ICMOS was developed. Due to the time-division color imaging scheme, motion artifacts may be introduced when a moving target is in the scene. To solve this problem, a deformable kernel prediction neural network (DKPNN) is proposed for joint denoising and motion artifact removal, and a data generation method which generates images with color-channel motion artifacts is also proposed to train the DKPNN. The results show that, compared with other denoising methods, the proposed DKPNN performed better both on generated noisy data and on real noisy data. Therefore, the proposed DKPNN is more suitable for color ICMOS denoising and motion artifact removal. A new exploration was made for low-light-level color imaging schemes.

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10

Shamshad, Fahad, M. Mohsin Riaz, and Abdul Ghafoor. "Poisson Denoising for Astronomical Images." Advances in Astronomy 2018 (June 10, 2018): 1–7. http://dx.doi.org/10.1155/2018/2417939.

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A denoising scheme for astronomical color images/videos corrupted with Poisson noise is proposed. The scheme employs the concept of Exponential Principal Component Analysis and sparsity of image patches. The color space RGB is converted to YCbCr and K-means++ clustering is applied on luminance component only. The cluster centers are used for chromatic components to improve the computational efficiency. For videos, the information of both spatial and temporal correlations improves the denoising. Simulation results verify the significance of proposed scheme in both visual and quantitative manner.

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11

Russo, Fabrizio. "Performance Evaluation of Noise Reduction Filters for Color Images through Normalized Color Difference (NCD) Decomposition." ISRN Machine Vision 2014 (January 22, 2014): 1–11. http://dx.doi.org/10.1155/2014/579658.

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Removing noise without producing image distortion is the challenging goal for any image denoising filter. Thus, the different amounts of residual noise and unwanted blur should be evaluated to analyze the actual performance of a denoising process. In this paper a novel full-reference method for measuring such features in color images is presented. The proposed approach is based on the decomposition of the normalized color difference (NCD) into three components that separately take into account different classes of filtering errors such as the inaccuracy in filtering noise pulses, the inaccuracy in reducing Gaussian noise, and the amount of collateral distortion. Computer simulations show that the proposed method offers significant advantages over other measures of filtering performance in the literature, including the recently proposed vector techniques.

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12

Singh, Amandeep, Gaurav Sethi, and G. S. Kalra. "Comparative Analysis of Color Image Denoising Techniques." International Journal of Engineering Research and Technology 13, no. 10 (October 31, 2020): 2761. http://dx.doi.org/10.37624/ijert/13.10.2020.2761-2767.

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13

Wang, Gaihua, Hu Zhu, and Yunyan Wang. "Fuzzy decision filter for color images denoising." Optik 126, no. 20 (October 2015): 2428–32. http://dx.doi.org/10.1016/j.ijleo.2015.06.005.

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14

Rousselle, Fabrice, Marco Manzi, and Matthias Zwicker. "Robust Denoising using Feature and Color Information." Computer Graphics Forum 32, no. 7 (October 2013): 121–30. http://dx.doi.org/10.1111/cgf.12219.

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15

Sun, Xin, Ning He, Yu-Qing Zhang, Xue-Yan Zhen, Ke Lu, and Xiu-Ling Zhou. "Color Image Denoising Based on Guided Filter and Adaptive Wavelet Threshold." Applied Computational Intelligence and Soft Computing 2017 (2017): 1–11. http://dx.doi.org/10.1155/2017/5835020.

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In the process of denoising color images, it is very important to enhance the edge and texture information of the images. Image quality can usually be improved by eliminating noise and enhancing contrast. Based on the adaptive wavelet threshold shrinkage algorithm and considering structural characteristics on the basis of color image denoising, this paper describes a method that further enhances the edge and texture details of the image using guided filtering. The use of guided filtering allows edge details that cannot be discriminated in grayscale images to be preserved. The noisy image is decomposed into low-frequency and high-frequency subbands using discrete wavelets, and the contraction function of threshold shrinkage is selected according to the energy in the vicinity of the wavelet coefficients. Finally, the edge and texture information of the denoised color image are enhanced by guided filtering. When the guiding image is the original noiseless image itself, the guided filter can be used as a smoothing operator for preserving edges, resulting in a better effect than bilateral filtering. The proposed method is compared with the adaptive wavelet threshold shrinkage denoising algorithm and the bilateral filtering algorithm. Experimental results show that the proposed method achieves superior color image denoising compared to these conventional techniques.

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16

Jia, Fan, Liyan Ma, Yijin Yang, and Tieyong Zeng. "Pixel-Attention CNN With Color Correlation Loss for Color Image Denoising." IEEE Signal Processing Letters 28 (2021): 1600–1604. http://dx.doi.org/10.1109/lsp.2021.3100263.

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17

Zeng, Jun, and Dehua Li. "Color image edge detection method using VTV denoising and color difference." Optik 123, no. 22 (November 2012): 2072–75. http://dx.doi.org/10.1016/j.ijleo.2011.10.009.

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18

Kim, Yeahwon, Hohyung Ryu, Sunmi Lee, and Yeon Ju Lee. "Joint Demosaicing and Denoising Based on Interchannel Nonlocal Mean Weighted Moving Least Squares Method." Sensors 20, no. 17 (August 20, 2020): 4697. http://dx.doi.org/10.3390/s20174697.

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Nowadays, the sizes of pixel sensors in digital cameras are decreasing as the resolution of the image sensor increases. Due to the decreased size, the pixel sensors receive less light energy, which makes it more sensitive to thermal noise. Even a small amount of noise in the color filter array (CFA) can have a significant effect on the reconstruction of the color image, as two-thirds of the missing data would have to be reconstructed from noisy data; because of this, direct denoising would need to be performed on the raw CFA to obtain a high-resolution color image. In this paper, we propose an interchannel nonlocal weighted moving least square method for the noise removal of the raw CFA. The proposed method is our first attempt of applying a two dimensional (2-D) polynomial approximation to denoising the CFA. Previous works make use of 2-D linear or directional 1-D polynomial approximations. The reason that 2-D polynomial approximation methods have not been applied to this problem is the difficulty of the weight control in the 2-D polynomial approximation method, as a small amount of noise can have a large effect on the approximated 2-D shape. This makes CFA denoising more important, as the approximated 2-D shape has to be reconstructed from only one-third of the original data. To address this problem, we propose a method that reconstructs the approximated 2-D shapes corresponding to the RGB color channels based on the measure of the similarities of the patches directly on the CFA. By doing so, the interchannel information is incorporated into the denoising scheme, which results in a well-controlled and higher order of polynomial approximation of the color channels. Compared to other nonlocal-mean-based denoising methods, the proposed method uses an extra reproducing constraint, which guarantees a certain degree of the approximation order; therefore, the proposed method can reduce the number of false reconstruction artifacts that often occur in nonlocal-mean-based denoising methods. Experimental results demonstrate the performance of the proposed algorithm.

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19

Fuller, Megan M., and Jae S. Lim. "A Color Image Model with Applications to Denoising." Electronic Imaging 2017, no. 18 (January 29, 2017): 177–83. http://dx.doi.org/10.2352/issn.2470-1173.2017.18.color-057.

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20

Sarode, M. V., and P. R. Deshmukh. "Image Sequence Denoising with Motion Estimation in Color Image Sequences." Engineering, Technology & Applied Science Research 1, no. 6 (December 10, 2011): 139–43. http://dx.doi.org/10.48084/etasr.54.

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In this paper, we investigate the denoising of image sequences i.e. video, corrupted with Gaussian noise and Impulse noise. In relation to single image denoising techniques, denoising of sequences aims to utilize the temporal dimension. This approach gives faster algorithms and better output quality. This paper focuses on the removal of different types of noise introduced in image sequences while transferring through network systems and video acquisition. The approach introduced consists of motion estimation, motion compensation, and filtering of image sequences. Most of the estimation approaches proposed deal mainly with monochrome video. The most usual way to apply them in color image sequences is to process each color channel separately. In this paper, we also propose a simple, accompanying method to extract the moving objects. Our experimental results on synthetic and natural images verify our arguments. The proposed algorithm’s performance is experimentally compared with a previous method, demonstrating comparable results.

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21

Hua, Gang, and Daihong Jiang. "A New Method of Image Denoising for Underground Coal Mine Based on the Visual Characteristics." Journal of Applied Mathematics 2014 (2014): 1–7. http://dx.doi.org/10.1155/2014/362716.

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Affected by special underground circumstances of coal mine, the image clarity of most images captured in the mine is not very high, and a large amount of image noise is mingled with the images, which brings further downhole images processing many difficulties. Traditional image denoising method easily leads to blurred images, and the denoising effect is not very satisfactory. Aimed at the image characteristics of low image illumination and large amount of noise and based on the characteristics of color detail blindness and simultaneous contrast of human visual perception, this paper proposes a new method for image denoising based on visual characteristics. The method uses CIELab uniform color space to dynamically and adaptively decide the filter weights, thereby reducing the damage to the image contour edges and other details, so that the denoised image can have a higher clarity. Experimental results show that this method has a brilliant denoising effect and can significantly improve the subjective and objective picture quality of downhole images.

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22

Rani, S., Y. Chabrra, and K. Malik. "An Improved Denoising Algorithm for Removing Noise in Color Images." Engineering, Technology & Applied Science Research 12, no. 3 (June 6, 2022): 8738–44. http://dx.doi.org/10.48084/etasr.4952.

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Noise has a significant impact on image quality in a variety of applications, including machine vision and object recognition. Denoising is crucial for successful image processing since noisy pictures lead to erroneous findings and segmentation and enhancement mistakes. Existing methods were primarily developed for grayscale image denoising and are unable to detect all damaged pixels in an image effectively. This paper proposes a sequential ROAD-TGM-HT method to suppress impulsive noise in color image denoising. The noisy pixel location is detected using the consecutive method in the first step, and the distorted value of the damaged pixel is reconstructed in the second stage, followed by the Hough transform for the remaining undetected pixels. Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM) were used to analyze the qualitative and quantitative performance. ROAD-TGM-HT excels on color images with noise levels ranging from 0.10 to 0.70, as per PSNR and SSIM qualitative data.

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23

Guo, Xue, Feng Liu, and Xuetao Tian. "Gaussian noise level estimation for color image denoising." Journal of the Optical Society of America A 38, no. 8 (July 16, 2021): 1150. http://dx.doi.org/10.1364/josaa.426092.

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24

Sun, Jian, and Zingben Xu. "Color Image Denoising via Discriminatively Learned Iterative Shrinkage." IEEE Transactions on Image Processing 24, no. 11 (November 2015): 4148–59. http://dx.doi.org/10.1109/tip.2015.2448352.

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25

Dinh, Khanh Quoc, Thuong Nguyen Canh, and Byeungwoo Jeon. "Color Image Denoising via Cross-Channel Texture Transferring." IEEE Signal Processing Letters 23, no. 8 (August 2016): 1071–75. http://dx.doi.org/10.1109/lsp.2016.2580711.

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26

Dai, Jingjing, Oscar C. Au, Lu Fang, Chao Pang, Feng Zou, and Jiali Li. "Multichannel Nonlocal Means Fusion for Color Image Denoising." IEEE Transactions on Circuits and Systems for Video Technology 23, no. 11 (November 2013): 1873–86. http://dx.doi.org/10.1109/tcsvt.2013.2269020.

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27

Muth, Stéphan, Sarah Dort, Igal A. Sebag, Marie-Josée Blais, and Damien Garcia. "Unsupervised dealiasing and denoising of color-Doppler data." Medical Image Analysis 15, no. 4 (August 2011): 577–88. http://dx.doi.org/10.1016/j.media.2011.03.003.

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28

Gai, Shan. "Multichannel image denoising using color monogenic curvelet transform." Soft Computing 22, no. 2 (September 26, 2016): 635–44. http://dx.doi.org/10.1007/s00500-016-2361-1.

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29

Lee, Hwea Yee, Wai Lam Hoo, and Chee Seng Chan. "Color video denoising using epitome and sparse coding." Expert Systems with Applications 42, no. 2 (February 2015): 751–59. http://dx.doi.org/10.1016/j.eswa.2014.08.033.

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30

Moreno, Rodrigo, Miguel Angel Garcia, Domenec Puig, and Carme Julià. "Edge-preserving color image denoising through tensor voting." Computer Vision and Image Understanding 115, no. 11 (November 2011): 1536–51. http://dx.doi.org/10.1016/j.cviu.2011.07.005.

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31

Gai, Shan, Yong Zhang, Cihui Yang, Lei Wang, and Jiehua Zhou. "Color monogenic wavelet transform for multichannel image denoising." Multidimensional Systems and Signal Processing 28, no. 4 (June 2, 2016): 1463–80. http://dx.doi.org/10.1007/s11045-016-0426-z.

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32

Li, Xin. "On modeling interchannel dependency for color image denoising." International Journal of Imaging Systems and Technology 17, no. 3 (2007): 163–73. http://dx.doi.org/10.1002/ima.20112.

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33

Keren, Daniel, and Anna Gotlib. "Denoising Color Images Using Regularization and “Correlation Terms”." Journal of Visual Communication and Image Representation 9, no. 4 (December 1998): 352–65. http://dx.doi.org/10.1006/jvci.1998.0392.

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34

Na-Ping, Chen, and Zeng You-Dong. "A Color Image Denoising and Enhancement Model Based on CB Color Model." Journal of Algorithms & Computational Technology 2, no. 1 (March 2008): 35–48. http://dx.doi.org/10.1260/174830108784300420.

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35

Arumugham, Rajamani, Krishnaveni Vellingiri, Wassim Ferose Habeebrakuman, and Kalaikamal Mohan. "A NEW DENOISING APPROACH FOR THE REMOVAL OF IMPULSE NOISE FROM COLOR IMAGES AND VIDEO SEQUENCES." Image Analysis & Stereology 31, no. 3 (November 28, 2012): 185. http://dx.doi.org/10.5566/ias.v31.p185-191.

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In this paper a novel Lone Diagonal Sorting (LDS) algorithm for denoising color images and videos co-rrupted with salt and pepper noise is proposed. The proposed lone diagonal sorting algorithm uses diagonal sorting alone for denoising of impulse noise. The algorithm has been implemented and tested for various color images and video signals and appreciable performance in terms of PSNR, MSE and SSIM is obtained. Our algorithm has been compared with other standard algorithms. A drastic improvement in the computational time has been achieved without compromising much on the visual quality after reconstruction.

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36

Guo, Yulong, Qingsheng Bi, Yuan Li, Chenggong Du, Junchang Huang, Weiqiang Chen, Lingfei Shi, and Guangxing Ji. "Sparse Representing Denoising of Hyperspectral Data for Water Color Remote Sensing." Applied Sciences 12, no. 15 (July 26, 2022): 7501. http://dx.doi.org/10.3390/app12157501.

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Hyperspectral data are important for water color remote sensing. The inevitable noise will devalue its application. In this study, we developed a 1-D denoising method for water hyperspectral data, based on sparse representing. The denoising performance was compared with three commonly used methods in simulated and real datasets. The results indicate that: (1) sparse representing can successfully decompose the hyperspectral water-surface reflectance signal from random noises; (2) the proposed method exhibited better performance compared with the other three methods in different input signal-to-noise ratio (SNR) levels; (3) the proposed method effectively erased abnormal spectral vibrations of field-measured and remote-sensing hyperspectral data; (4) whilst the method is built in 1-D, it can still control the salt-and-pepper noise of PRISMA hyperspectral image. In conclusion, the proposed denoising method can improve the hyperspectral data of an optically complex water body and offer a better data source for the remote monitoring of water color.

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37

Starosolski, Roman. "Reversible denoising and lifting based color component transformation for lossless image compression." Multimedia Tools and Applications 79, no. 17-18 (November 3, 2019): 11269–94. http://dx.doi.org/10.1007/s11042-019-08371-w.

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Abstract An undesirable side effect of reversible color space transformation, which consists of lifting steps (LSs), is that while removing correlation it contaminates transformed components with noise from other components. Noise affects particularly adversely the compression ratios of lossless compression algorithms. To remove correlation without increasing noise, a reversible denoising and lifting step (RDLS) was proposed that integrates denoising filters into LS. Applying RDLS to color space transformation results in a new image component transformation that is perfectly reversible despite involving the inherently irreversible denoising; the first application of such a transformation is presented in this paper. For the JPEG-LS, JPEG 2000, and JPEG XR standard algorithms in lossless mode, the application of RDLS to the RDgDb color space transformation with simple denoising filters is especially effective for images in the native optical resolution of acquisition devices. It results in improving compression ratios of all those images in cases when unmodified color space transformation either improves or worsens ratios compared with the untransformed image. The average improvement is 5.0–6.0% for two out of the three sets of such images, whereas average ratios of images from standard test-sets are improved by up to 2.2%. For the efficient image-adaptive determination of filters for RDLS, a couple of fast entropy-based estimators of compression effects that may be used independently of the actual compression algorithm are investigated and an immediate filter selection method based on the detector precision characteristic model driven by image acquisition parameters is introduced.

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38

Boutekkouk, Fateh. "Digital Color Image Processing Using Intuitionistic Fuzzy Hypergraphs." International Journal of Computer Vision and Image Processing 11, no. 3 (July 2021): 21–40. http://dx.doi.org/10.4018/ijcvip.2021070102.

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Hypergraphs are considered a useful mathematical tool for digital image processing and analysis since they can represent digital images as complex relationships between pixels or block of pixels. The notion of hypergraphs has been extended in fuzzy theory leading to the concept of fuzzy hypergraphs, then in intuitionistic fuzzy theory conducting to the concept of intuitionistic fuzzy hypergraphs or IFHG. The latter is very suitable to model digital images with uncertain or imprecise knowledge. This paper deals with color image denoising, segmentation, and edge detection in a color image initially represented in RGB space using intuitionistic fuzzy hypergraphs. First, the RGB image is transformed to HLS space resulting in three separated components. Then each component is intuitionistically fuzzified based on entropy measure from which an intuitionistic fuzzy hypergraph is generated automatically. The generated hypergraphs will be used for denoising, segmentation, and edge detection.

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39

Kwan, Chiman, and Jude Larkin. "Comparison of Denoising Algorithms for Demosacing Low Lighting Images Using CFA 2.0." Signal & Image Processing : An International Journal 11, no. 5 (October 30, 2020): 37–60. http://dx.doi.org/10.5121/sipij.2020.11503.

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In modern digital cameras, the Bayer color filter array (CFA) has been widely used. It is also widely known as CFA 1.0. However, Bayer pattern is inferior to the red-green-blue-white (RGBW) pattern, which is also known as CFA 2.0, in low lighting conditions in which Poisson noise is present. It is well known that demosaicing algorithms cannot effectively deal with Poisson noise and additional denoising is needed in order to improve the image quality. In this paper, we propose to evaluate various conventional and deep learning based denoising algorithms for CFA 2.0 in low lighting conditions. We will also investigate the impact of the location of denoising, which refers to whether the denoising is done before or after a critical step of demosaicing. Extensive experiments show that some denoising algorithms can indeed improve the image quality in low lighting conditions. We also noticed that the location of denoising plays an important role in the overall demosaicing performance.

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40

Wu, Zeju, Yang Ji, Lijun Song, and Jianyuan Sun. "Underwater Image Enhancement Based on Color Correction and Detail Enhancement." Journal of Marine Science and Engineering 10, no. 10 (October 17, 2022): 1513. http://dx.doi.org/10.3390/jmse10101513.

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To solve the problems of underwater image color deviation, low contrast, and blurred details, an algorithm based on color correction and detail enhancement is proposed. First, the improved nonlocal means denoising algorithm is used to denoise the underwater image. The combination of Gaussian weighted spatial distance and Gaussian weighted Euclidean distance is used as the index of nonlocal means denoising algorithm to measure the similarity of structural blocks. The improved algorithm can retain more edge features and texture information while maintaining noise reduction ability. Then, the improved U-Net is used for color correction. Introducing residual structure and attention mechanism into U-Net can effectively enhance feature extraction ability and prevent network degradation. Finally, a sharpening algorithm based on maximum a posteriori is proposed to enhance the image after color correction, which can increase the detailed information of the image without expanding the noise. The experimental results show that the proposed algorithm has a remarkable effect on underwater image enhancement.

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41

Sumathi, K., and Ch Hima Bindu. "Image Denoising in Wavelet Domain with Filtering and Thresholding." International Journal of Engineering & Technology 7, no. 3.34 (September 1, 2018): 327. http://dx.doi.org/10.14419/ijet.v7i3.34.19218.

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In this paper, the proposed method is implemented for removal of salt & pepper and Gaussian noise of black & white & color images toacquire the quality output. In this work initially wavelet coefficients are extracted for noisy images. Later apply denoise filteringtechnique on the high transform sub bands of noisy images (either color/ B & W) using new laplacian filters with 4 directions. Finallythreshold of an image is generated to extract denoisy coefficients. At last inverse of above subband coefficients can give denoise imagefor further processing. The proposed method is verified against various B & W/color images and it gives a better PSNR (Peak Signal toNoise Ratio) & MI (Mutual Information). These values are compared with different noise densities and analyzed visually.

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Gallegos-Funes, F., J. Martínez-Valdés, R. Cruz-Santiago, and J. López-Bonilla. "Wavelet Order Statistics Filters for Image Denoising." Journal of Scientific Research 1, no. 2 (April 22, 2009): 248–57. http://dx.doi.org/10.3329/jsr.v1i2.2311.

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This paper presents the wavelet order statistics filters for the removal of impulsive and speckle noise in color image applications. The proposed filtering scheme is defined as two filters in the wavelet domain to conform to the structure of a general filter that can be modified in some headings. The first filter is based on redundancy of approaches and the second one is the wavelet domain iterative center weighted median algorithm. With the structure of the proposed filter different implementations for the estimation of the noisy sample are carried out using different order statistics algorithms that by their good performance can be beneficial in color image processing applications. Keywords: Wavelet domain; Order statistics filters. © 2009 JSR Publications. ISSN: 2070-0237 (Print); 2070-0245 (Online). All rights reserved.DOI: 10.3329/jsr.v1i2.2311

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Irfan, Muhammad Abeer, and Enrico Magli. "Exploiting color for graph-based 3D point cloud denoising." Journal of Visual Communication and Image Representation 75 (February 2021): 103027. http://dx.doi.org/10.1016/j.jvcir.2021.103027.

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Baby, Alin Mary Varghese, and Dhanya S. N. "Design of A Bilateral Filter For Color Image Denoising." i-manager's Journal on Digital Signal Processing 3, no. 1 (March 15, 2015): 30–33. http://dx.doi.org/10.26634/jdp.3.1.3288.

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Zhu, Rong, and Yong Wang. "Color Image Denoising via Dictionary Learning and Sparse Representation." Journal of Computational and Theoretical Nanoscience 12, no. 10 (October 1, 2015): 3911–16. http://dx.doi.org/10.1166/jctn.2015.4302.

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Xu, Xudong, and Jinling Zhao. "Quaternion Mahalanobis Non-local Means for Color Image Denoising." Journal of Physics: Conference Series 1621 (August 2020): 012046. http://dx.doi.org/10.1088/1742-6596/1621/1/012046.

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Gai, Shan. "Color image denoising via monogenic matrix-based sparse representation." Visual Computer 35, no. 1 (November 15, 2017): 109–22. http://dx.doi.org/10.1007/s00371-017-1456-8.

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Zhang, Ying, Feng Zhang, and Ran Tao. "Multichannel color image denoising based on multiple dictionaries learning." Journal of Electronic Imaging 28, no. 02 (March 5, 2019): 1. http://dx.doi.org/10.1117/1.jei.28.2.023002.

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V, Umadevi, and Shaik Salma Begum. "A novel Entropy based Denoising algorithm on color videos." International Journal of Engineering Trends and Technology 29, no. 1 (November 25, 2015): 46–50. http://dx.doi.org/10.14445/22315381/ijett-v29p209.

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Nai-Xiang Lian, V. Zagorodnov, and Yap-Peng Tan. "Edge-preserving image denoising via optimal color space projection." IEEE Transactions on Image Processing 15, no. 9 (September 2006): 2575–87. http://dx.doi.org/10.1109/tip.2006.877409.

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