Results 281 to 290 of about 6,876,175 (322)

Global Image Denoising

IEEE Transactions on Image Processing, 2014
Most existing state-of-the-art image denoising algorithms are based on exploiting similarity between a relatively modest number of patches. These patch-based methods are strictly dependent on patch matching, and their performance is hamstrung by the ability to reliably find sufficiently similar patches.
Hossein, Talebi, Peyman, Milanfar
openaire   +2 more sources

Recorrupted-to-Recorrupted: Unsupervised Deep Learning for Image Denoising

Computer Vision and Pattern Recognition, 2021
Deep denoiser, the deep network for denoising, has been the focus of the recent development on image denoising. In the last few years, there is an increasing interest in developing unsupervised deep denoisers which only call unorganized noisy images ...
T. Pang, Huan Zheng, Yuhui Quan, Hui Ji
semanticscholar   +1 more source

NTIRE 2023 Challenge on Image Denoising: Methods and Results

2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2023
This paper reviews the NTIRE 2023 challenge on image denoising (σ = 50) with a focus on the proposed solutions and results. The aim is to obtain a network design capable to produce high-quality results with the best performance measured by PSNR for image
Yawei Li, Yulun Zhang, R. Timofte, L. Gool, Zhaopeng Tu, Kunpeng Du, Hailing Wang, Hanting Chen, Wei Li, Xiaofei Wang, Jie Hu, Yunhe Wang, Xiangyu Kong, Jinlong Wu, Dafeng Zhang, Jianxing Zhang, Shuai Liu, Furui Bai, Chaoyu Feng, Hao Wang, Yuqian Zhang, Guangqi Shao, Xiaotao Wang, Lei Lei, Rongjian Xu, Zhilu Zhang, Yunjin Chen, Dongwei Ren, W. Zuo, Qi Wu, Mingyan Han, Shen Cheng, Haipeng Li, Ting Jiang, Chengzhi Jiang, Xinpeng Li, Jinting Luo, Wenjie Lin, Leiting Yu, Haoqiang Fan, Shuaicheng Liu, Aditya Arora, Syed Waqas Zamir, Javier Vazquez-Corral, K. Derpanis, Michael S. Brown, Hao Li, Zhihao Zhao, Jin-shan Pan, Jiangxin Dong, Jinhui Tang, Bo Yang, Jingxiang Chen, LI, Xi Zhang, Zhaona Zhang, Jiahuan Ren, Zhicheng Ji, Kang Miao, Suiyi Zhao, Huan Zheng, Yanyan Wei, Kangliang Liu, Xiangcheng Du, Sijie Liu, Yi Zheng, Xingjiao Wu, Cheng Jin, Rajeev Irny, Sriharsha Koundinya, V. Kamath, Gaurav Khandelwal, S. A. Khowaja, Jiseok Yoon, I. Lee, Shijie Chen, Chengqiang Zhao, Huabin Yang, Zhongjian Zhang, Junjia Huang, Yanru Zhang   +80 more
semanticscholar   +1 more source

Multiscale Image Blind Denoising

IEEE Transactions on Image Processing, 2015
Arguably several thousands papers are dedicated to image denoising. Most papers assume a fixed noise model, mainly white Gaussian or Poissonian. This assumption is only valid for raw images. Yet, in most images handled by the public and even by scientists, the noise model is imperfectly known or unknown.
Marc, Lebrun, Miguel, Colom, Jean-Michel, Morel   +2 more
openaire   +2 more sources

GradNet Image Denoising

2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2020
High-frequency regions like edges compromise the image denoising performance. In traditional hand-crafted systems, image edges/textures were regularly used to restore the frequencies in these regions. However, this practice seems to be left forgotten in the deep learning era. In this paper, we revisit this idea of using the image gradient and introduce
Yang Liu, Saeed Anwar, Liang Zheng, Qi Tian   +3 more
openaire   +1 more source

Complexity-regularized image denoising

IEEE Transactions on Image Processing, 2001
Summary: We study a new approach to image denoising based on complexity regularization. This technique presents a flexible alternative to the more conventional \(l^2\), \(l^1\), and Besov regularization methods. Different complexity measures are considered, in particular those induced by state-of-the-art image coders.
Liu, Juan, Moulin, Pierre
openaire   +2 more sources

Adaptive Consistency Prior based Deep Network for Image Denoising

Computer Vision and Pattern Recognition, 2021
Recent studies have shown that deep networks can achieve promising results for image denoising. However, how to simultaneously incorporate the valuable achievements of traditional methods into the network design and improve network interpretability is ...
Chao Ren, Xiaohai He, Chuncheng Wang, Zhibo Zhao   +3 more
semanticscholar   +1 more source

Stochastic Image Denoising

Procedings of the British Machine Vision Conference 2009, 2009
We present a novel algorithm for image denoising. Our algorithm is based on random walks over arbitrary neighbourhoods surrounding a given pixel. The size and shape of each neighbourhood are determined by the configuration and similarity of nearby pixels.
Francisco Estrada, David Fleet, Allan Jepson   +2 more
openaire   +1 more source

Flex-DLD: Deep Low-Rank Decomposition Model With Flexible Priors for Hyperspectral Image Denoising and Restoration

IEEE Transactions on Image Processing
Hyperspectral images (HSIs) are composed of hundreds of contiguous waveband images, offering a wealth of spatial and spectral information. However, the practical use of HSIs is often hindered by the presence of complicated noise caused by various factors
Yurong Chen, Hui Zhang, Yaonan Wang, Yimin Yang, Jonathan Wu   +4 more
semanticscholar   +1 more source

U2D2Net: Unsupervised Unified Image Dehazing and Denoising Network for Single Hazy Image Enhancement

IEEE transactions on multimedia
Hazy images captured under ill-posed scenarios with scattering medium (i.e. haze, fog, or smoke) are contaminated in visibility. Inevitably, these images are further degraded by noises owing to real-world imaging.
Bosheng Ding, Ruiheng Zhang, Lixin Xu, Guanyu Liu, Shuo Yang, Yumeng Liu, Qi Zhang   +6 more
semanticscholar   +1 more source