Results 271 to 280 of about 6,876,175 (322)

Enhancing knee osteoarthritis detection with AI, image denoising, and optimized classification methods and the importance of physical therapy methods. [PDF]

PeerJ Comput Sci
Bugday B, Bingol H, Yildirim M, Alatas B.   +3 more
europepmc   +1 more source

Noise-aware dynamic image denoising and positron range correction for Rubidium-82 cardiac PET imaging via self-supervision. [PDF]

Med Image Anal
Xie H, Guo L, Velo A, Liu Z, Liu Q, Guo X, Zhou B, Chen X, Tsai YJ, Miao T, Xia M, Liu YH, Armstrong IS, Wang G, Carson RE, Sinusas AJ, Liu C.   +16 more
europepmc   +1 more source

Integrating Kalman filter noise residue into U-Net for robust image denoising: the KU-Net model. [PDF]

Sci Rep
Soniya S, Sriharipriya KC.
europepmc   +1 more source

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Image Denoising Via Sparse and Redundant Representations Over Learned Dictionaries

IEEE Transactions on Image Processing, 2006
We address the image denoising problem, where zero-mean white and homogeneous Gaussian additive noise is to be removed from a given image. The approach taken is based on sparse and redundant representations over trained dictionaries.
Michael Elad, M. Aharon
semanticscholar   +3 more sources

Hyperspectral Image Denoising via Tensor Low-Rank Prior and Unsupervised Deep Spatial–Spectral Prior

IEEE Transactions on Geoscience and Remote Sensing, 2022
Hyperspectral image (HSI) denoising is a fundamental task in remote sensing image processing, which is helpful for HSI subsequent applications, such as unmixing and classification.
Wei-Hao Wu, Ting-Zhu Huang, Xile Zhao, Jian-Li Wang, Yu-Bang Zheng   +4 more
semanticscholar   +3 more sources

Image denoising using deep CNN with batch renormalization

Neural Networks, 2020
Deep convolutional neural networks (CNNs) have attracted great attention in the field of image denoising. However, there are two drawbacks: (1) it is very difficult to train a deeper CNN for denoising tasks, and (2) most of deeper CNNs suffer from ...
Chunwei Tian, Yong Xu, W. Zuo
semanticscholar   +3 more sources

Image Denoising by Sparse 3-D Transform-Domain Collaborative Filtering

IEEE Transactions on Image Processing, 2007
Kostadin Dabov, A. Foi, V. Katkovnik, K. Egiazarian   +3 more
semanticscholar   +3 more sources

Hyperspectral Image Denoising: From Model-Driven, Data-Driven, to Model-Data-Driven

IEEE Transactions on Neural Networks and Learning Systems, 2023
Mixed noise pollution in HSI severely disturbs subsequent interpretations and applications. In this technical review, we first give the noise analysis in different noisy HSIs and conclude crucial points for programming HSI denoising algorithms.
Qiang Zhang, Yaming Zheng, Qiangqiang Yuan, Meiping Song, Haoyang Yu, Yi Xiao   +5 more
semanticscholar   +1 more source

Progressive Image Denoising

IEEE Transactions on Image Processing, 2014
Image denoising continues to be an active research topic. Although state-of-the-art denoising methods are numerically impressive and approch theoretical limits, they suffer from visible artifacts.While they produce acceptable results for natural images, human eyes are less forgiving when viewing synthetic images.
Claude, Knaus, Matthias, Zwicker
openaire   +2 more sources

Fractal image denoising

IEEE Transactions on Image Processing, 2003
Over the past decade, there has been significant interest in fractal coding for the purpose of image compression. However, applications of fractal-based coding to other aspects of image processing have received little attention. We propose a fractal-based method to enhance and restore a noisy image.
Mohsen, Ghazel, George H, Freeman, Edward R, Vrscay   +2 more
openaire   +2 more sources