Results 221 to 230 of about 17,318 (265)
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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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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
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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 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 0007, 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 0007, K. J. Ray Liu
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Improved Denoising Auto-Encoders for Image Denoising
2018 11th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI), 2018Image denoising is an important pre-processing step in image analysis. Various denoising algorithms, such as BM3D, PCD and K-SVD, obtain remarkable effects. Recently a deep denoising auto-encoder has been proposed and shown excellent performance compared to conventional image denoising algorithms.
Qian Xiang, Xuliang Pang
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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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Image denoising with complex ridgelets
Pattern Recognition, 2007zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Chen, Guangyi, Kégl, Balázs
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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.
Juan Liu, Pierre Moulin
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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
openaire +1 more source