Results 241 to 250 of about 161,991 (287)
Some of the next articles are maybe not open access.
IEEE Transactions on Image Processing, 2010
Image denoising has been a well studied problem in the field of image processing. Yet researchers continue to focus attention on it to better the current state-of-the-art. Recently proposed methods take different approaches to the problem and yet their denoising performances are comparable.
Priyam Chatterjee, Peyman Milanfar
exaly +3 more sources
Image denoising has been a well studied problem in the field of image processing. Yet researchers continue to focus attention on it to better the current state-of-the-art. Recently proposed methods take different approaches to the problem and yet their denoising performances are comparable.
Priyam Chatterjee, Peyman Milanfar
exaly +3 more sources
Blind Denoising Autoencoder [PDF]
IEEE Transactions on Neural Networks and Learning Systems, 2019 The final version accepted at IEEE Transactions on Neural Networks and Learning ...
Angshul Majumdar
exaly +4 more sources
Computational Statistics, 2007
Iterative denoising is a data mining technology for analysis of large heterogeneous datasets, e.g., sets of text documents. The result of it is a hierarchical divisive cluster tree with visual representation of each node. The specific feature of this technology is that the features for the clustering are extracted from data at each node of the ...
Giles, Kendall E. +3 more
openaire +1 more source
Iterative denoising is a data mining technology for analysis of large heterogeneous datasets, e.g., sets of text documents. The result of it is a hierarchical divisive cluster tree with visual representation of each node. The specific feature of this technology is that the features for the clustering are extracted from data at each node of the ...
Giles, Kendall E. +3 more
openaire +1 more source
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
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
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
openaire +2 more sources
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
openaire +2 more sources
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
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