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Overview of Research on Digital Image Denoising Methods [PDF]
SensorsDuring image collection, images are often polluted by noise because of imaging conditions and equipment limitations. Images are also disturbed by external noise during compression and transmission, which adversely affects consequent processing, like ...
Jing Mao +3 more
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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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Self-Supervised Joint Learning for pCLE Image Denoising [PDF]
SensorsProbe-based confocal laser endoscopy (pCLE) has emerged as a powerful tool for disease diagnosis, yet it faces challenges such as the formation of hexagonal patterns in images due to the inherent characteristics of fiber bundles.
Kun Yang +4 more
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Efficient real-world image denoising using multi-scale gaussian pyramids [PDF]
Scientific ReportsThe field of image denoising has undergone significant advancements over the years. Recently, Convolutional Neural Networks (CNN) based denoising methods have shown remarkable performance in image denoising.
Asha Rani, Rosepreet Kaur Bhogal
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Image denoising method integrating ridgelet transform and improved wavelet threshold. [PDF]
PLoS ONEIn the field of image processing, common noise types include Gaussian noise, salt and pepper noise, speckle noise, uniform noise and pulse noise. Different types of noise require different denoising algorithms and techniques to maintain image quality and
Bingbing Li, Yao Cong, Hongwei Mo
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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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