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IEEE Transactions on Image Processing, 2014 Image denoising continues to be an active research topic. Although state-of-the-art denoising methods are numerically impressive and approch theoretical limits, they suffer from visible artifacts.While they produce acceptable results for natural images ...
Knaus, Claude, Zwicker, Matthias
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Adversarial Gaussian Denoiser for Multiple-Level Image Denoising [PDF]
Sensors, 2021 Image denoising is a challenging task that is essential in numerous computer vision and image processing problems. This study proposes and applies a generative adversarial network-based image denoising training architecture to multiple-level Gaussian ...
Aamir Khan +4 more
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Dual-Domain Image Denoising [PDF]
2013 IEEE International Conference on Image Processing, 2013 Image denoising methods have been implemented in both spatial and transform domains. Each domain has its advantages and shortcomings, which can be complemented by each other.
Matthias Zwicker +3 more
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Image denoising with directional bases [PDF]
2007 IEEE International Conference on Image Processing, 2007 Directional information is an important component of both natural and synthetic images, and it is exploited in many image processing applications. Directional basis analysis is used to capture significant structural information.
Park, Heechan +3 more
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Overview of Image Denoising Methods
Jisuanji kexue yu tansuo, 2021 In real scenes, due to the imperfections of equipment and systems or the existence of low-light environments, the collected images are noisy. The images will also be affected by additional noise during the compression and transmission process, which will
LIU Liping, QIAO Lele, JIANG Liucheng
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Applied Sciences, 2022 Supervised image denoising methods based on deep neural networks require a large amount of noisy-clean or noisy image pairs for network training. Thus, their performance drops drastically when the given noisy image is significantly different from the ...
Shaoping Xu +5 more
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Denoising an Image by Denoising Its Curvature Image [PDF]
SIAM Journal on Imaging Sciences, 2014 The first author acknowledges partial support by European Research Council, Starting Grant ref. 306337, and/nby Spanish grants AACC, ref. TIN2011-15954-E, and Plan Nacional, ref. TIN2012-38112. The second author was supported in part by NSF-DMS #0915219.
Marcelo BertalmÃo, Stacey Levine
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Image Denoising Using Hybrid Deep Learning Approach and Self-Improved Orca Predation Algorithm
Technologies, 2023 Image denoising is a critical task in computer vision aimed at removing unwanted noise from images, which can degrade image quality and affect visual details.
Rusul Sabah Jebur +4 more
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Unpaired Image Denoising [PDF]
2020 IEEE International Conference on Image Processing (ICIP), 2020 Deep learning approaches in image processing predominantly resort to supervised learning. A majority of methods for image denoising are no exception to this rule and hence demand pairs of noisy and corresponding clean images. Only recently has there been the emergence of methods such as Noise2Void, where a deep neural network learns to denoise solely ...
Priyatham Kattakinda +1 more
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Impact of Traditional and Embedded Image Denoising on CNN-Based Deep Learning
Applied Sciences, 2023 In digital image processing, filtering noise is an important step for reconstructing a high-quality image for further processing such as object segmentation, object detection, and object recognition.
Roopdeep Kaur +2 more
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