Results 291 to 300 of about 116,500 (328)
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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, Peyman, Milanfar
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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, Peyman, Milanfar
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Multiscale Image Blind Denoising
IEEE Transactions on Image Processing, 2015Arguably 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 +2 more
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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 +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 +3 more
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Complexity-regularized image denoising
IEEE Transactions on Image Processing, 2001Summary: 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
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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 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 Estrada +2 more
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2017 International Conference on Electrical, Electronics, Communication, Computer, and Optimization Techniques (ICEECCOT), 2017
In recent years, there is a significant development in the field of image denoising. It involves the methods from both spatial domain as well as transform domain. In this paper, we are proposing a hybrid based denoising technique applicable for an image corrupted by Gaussian noise. The method uses both spatial and transforms domains.
B N Aravind, K V Suresh
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In recent years, there is a significant development in the field of image denoising. It involves the methods from both spatial domain as well as transform domain. In this paper, we are proposing a hybrid based denoising technique applicable for an image corrupted by Gaussian noise. The method uses both spatial and transforms domains.
B N Aravind, K V Suresh
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SCS 2003. International Symposium on Signals, Circuits and Systems. Proceedings (Cat. No.03EX720), 2004
The SAR images are corrupted by speckle noise. A new denoising method for this kind of images is reported in this paper. Inspired from the classical Donoho's denoising method, the procedure presented in this paper uses a new type of discrete wavelet transform, entitled Diversity Enhanced Discrete Wavelet Transform, DEDWT and a new filtering strategy ...
M. Kovaci, D. Isar, A. Isar
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The SAR images are corrupted by speckle noise. A new denoising method for this kind of images is reported in this paper. Inspired from the classical Donoho's denoising method, the procedure presented in this paper uses a new type of discrete wavelet transform, entitled Diversity Enhanced Discrete Wavelet Transform, DEDWT and a new filtering strategy ...
M. Kovaci, D. Isar, A. Isar
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IEEE Transactions on Circuits and Systems for Video Technology, 2013
Based on the observation that every small window in a natural image has many similar windows in the same image, the nonlocal denoising methods perform denoising by weighted averaging all the pixels in a nonlocal window and have achieved very promising denoising results.
Yan Chen, K. J. Ray Liu
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Based on the observation that every small window in a natural image has many similar windows in the same image, the nonlocal denoising methods perform denoising by weighted averaging all the pixels in a nonlocal window and have achieved very promising denoising results.
Yan Chen, K. J. Ray Liu
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2010
We present a novel probabilistic algorithm for image noise removal. The algorithm is inspired by the Google PageRank algorithm for ranking hypertextual world wide web documents and based upon considering the topological structure of the photometric similarity between image pixels. We provide computationally efficient strategies for obtaining a solution
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We present a novel probabilistic algorithm for image noise removal. The algorithm is inspired by the Google PageRank algorithm for ranking hypertextual world wide web documents and based upon considering the topological structure of the photometric similarity between image pixels. We provide computationally efficient strategies for obtaining a solution
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