Results 31 to 40 of about 18,244,520 (347)
Image denoising algorithm of social network based on multifeature fusion
Journal of Intelligent Systems, 2022 A social network image denoising algorithm based on multifeature fusion is proposed. Based on the multifeature fusion theory, the process of social network image denoising is regarded as the fitting process of neural network, and a simple and efficient ...
Zhao Lanfei, Zhu Qidan
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Non-local clustering via sparse prior for sports image denoising
EAI Endorsed Transactions on Scalable Information Systems, 2022 This article has been retracted, and the retraction notice can be found here: http://dx.doi.org/10.4108/eai.8-4-2022.173794. Image denoising is very important in image preprocessing. In order to introduce the priori information of external clean image
Ying Zhang
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Image Denoising With Generative Adversarial Networks and its Application to Cell Image Enhancement
IEEE Access, 2020 This paper proposes an image denoising training framework based on Wasserstein Generative Adversarial Networks (WGAN) and applies it to cell image denoising. Cell image denoising is a challenging task which has high requirement on the recovery of feature
Songkui Chen +3 more
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Blind2Unblind: Self-Supervised Image Denoising with Visible Blind Spots [PDF]
Computer Vision and Pattern Recognition, 2022 Real noisy-clean pairs on a large scale are costly and difficult to obtain. Meanwhile, supervised denoisers trained on synthetic data perform poorly in practice.
Zejin Wang +3 more
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Self-Supervised Image Denoising for Real-World Images With Context-Aware Transformer [PDF]
IEEE Access, 2023 In recent years, the development of deep learning has been pushing image denoising to a new level. Among them, self-supervised denoising is increasingly popular because it does not require any prior knowledge. Most of the existing self-supervised methods
Dan Zhang, Fangfang Zhou
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Random Sub-Samples Generation for Self-Supervised Real Image Denoising [PDF]
IEEE International Conference on Computer Vision, 2023 With sufficient paired training samples, the supervised deep learning methods have attracted much attention in image denoising because of their superior performance.
Yi-Zhong Pan +4 more
semanticscholar +1 more source
EURASIP Journal on Image and Video Processing, 2017 Background Digital images are captured using sensors during the data acquisition phase, where they are often contaminated by noise (an undesired random signal).
Monagi H. Alkinani, Mahmoud R. El-Sakka
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A Geometric Structure Based Non Local Mean Image Denoising Algorithm
IEEE Access, 2023 With the widespread application of image recognition technology, the commercial application value of image denoising is gradually increasing. To optimize the performance of non local mean image denoising algorithms, the similarity of image blocks in this
Lei Shi
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Plug-and-Play Priors for Model Based Reconstruction [PDF]
, 2013 Model-based reconstruction is a powerful framework for solving a variety of inverse problems in imaging. The method works by combining a forward model of the imaging system with a prior model of the image itself, and the reconstruction is then computed ...
Bouman, Charles A. +5 more
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A Novel Gray Image Denoising Method Using Convolutional Neural Network
IEEE Access, 2022 In order to make the image denoising more effective in high noise level environment, we propose a gray image denoising method using convolutional neural network (ConvNet). By constructing symmetric and dilated convolutional residual network and combining
Yizhen Meng, Jun Zhang
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