Results 261 to 270 of about 43,627 (311)
Low-dose computed tomography image denoising using pixel level non-local self-similarity prior with non-local means for healthcare informatics. [PDF]
Sci RepLepcha DC +6 more
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NAF-MEEF: A Nonlinear Activation-Free Network Based on Multi-Scale Edge Enhancement and Fusion for Railway Freight Car Image Denoising. [PDF]
Sensors (Basel)Chen J, Yue J, Zhou H, Hu Z.
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Edge and texture aware image denoising using median noise residue U-net with hand-crafted features. [PDF]
PeerJ Comput SciS S, C SK.
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Robust whole-body PET image denoising using 3D diffusion models: evaluation across various scanners, tracers, and dose levels. [PDF]
Eur J Nucl Med Mol ImagingYu B +6 more
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Progressive Image Denoising [PDF]
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
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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 0249 +3 more
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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 0249 +3 more
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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 +2 more
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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 +2 more
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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 J. Estrada +2 more
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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 J. Estrada +2 more
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On cooperative image denoising
2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2011In this paper we suggest how several competing image denoising algorithms, differing in design parameters, or even in design principles, can be combined together to yield a better and more reliable denoising algorithm. The proposed fusion mechanism allows one to combine practically all kinds of noise reduction tools.
Maciej Niedzwiecki, Szymon Gackowski
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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 Esfandarani +1 more
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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 Esfandarani +1 more
openaire +2 more sources