Results 11 to 20 of about 11,782,843 (259)
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. This paper presents an empirical study of image denoising with directional bases. We consider two distinct approaches.
Heechan Park, Graham R. Martin, Zhen Yao
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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. 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
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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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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EAI Endorsed Transactions on Scalable Information Systems, 2022 This article has been retracted, and the retraction notice can be found here: http://dx.doi.org/10.4108/eai.8-4-2022.173794.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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Flashlight CNN Image Denoising [PDF]
2020 28th European Signal Processing Conference (EUSIPCO), 2021 This paper proposes a learning-based denoising method called FlashLight CNN (FLCNN) that implements a deep neural network for image denoising. The proposed approach is based on deep residual networks and inception networks and it is able to leverage many more parameters than residual networks alone for denoising grayscale images corrupted by additive ...
Pham Huu Thanh Binh +2 more
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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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WINNet: wavelet-inspired invertible network for image denoising [PDF]
, 2021 Image denoising aims to restore a clean image from an observed noisy one. Model-based image denoising approaches can achieve good generalization ability over different noise levels and are with high interpretability. Learning-based approaches are able to
Huang, Jun-Jie +3 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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