Results 11 to 20 of about 18,244,520 (347)
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. State-of-the-art methods like block-matching 3D filtering (BM3D) therefore combine both domains. However, implementation of such methods is not trivial.
Claude Knaus, Matthias Zwicker
openaire +4 more sources
Multi-stage image denoising with the wavelet transform [PDF]
Pattern Recognition, 2022 Deep convolutional neural networks (CNNs) are used for image denoising via automatically mining accurate structure information. However, most of existing CNNs depend on enlarging depth of designed networks to obtain better denoising performance, which ...
Chunwei Tian +5 more
semanticscholar +1 more source
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
doaj +1 more source
Practical Blind Image Denoising via Swin-Conv-UNet and Data Synthesis [PDF]
Machine Intelligence Research, 2022 While recent years have witnessed a dramatic upsurge of exploiting deep neural networks toward solving image denoising, existing methods mostly rely on simple noise assumptions, such as additive white Gaussian noise (AWGN), JPEG compression noise and ...
K. Zhang +7 more
semanticscholar +1 more source
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
openaire +2 more sources
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
doaj +1 more source
Dual Residual Attention Network for Image Denoising [PDF]
Pattern Recognition, 2023 In image denoising, deep convolutional neural networks (CNNs) can obtain favorable performance on removing spatially invariant noise. However, many of these networks cannot perform well on removing the real noise (i.e.
Wencong Wu +4 more
semanticscholar +1 more source
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
doaj +1 more source
Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising [PDF]
IEEE Transactions on Image Processing, 2016 The discriminative model learning for image denoising has been recently attracting considerable attentions due to its favorable denoising performance.
K. Zhang +4 more
semanticscholar +1 more source
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
openaire +1 more source