Results 271 to 280 of about 43,627 (311)
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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
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Curvelet image denoising of mammogram images
International Journal of Medical Engineering and Informatics, 2013Mammography, the most commonly used diagnostic technique is used for early detection of breast cancer. As mammograms are low contrast and noisy images, it is essential to reduce noise while preserving fine details and edges. In order to obtain efficient diagnosis, a constructive analysis curvelet is used to provide optimal sparse representation of ...
Malar Elangeeran +4 more
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An image topic model for image denoising
Neurocomputing, 2015Abstract Topic model is a powerful tool for the basic document or image processing tasks. In this study we introduce a novel image topic model, called Latent Patch Model (LPM), which is a generative Bayesian model and assumes that the image and pixels are connected by a latent patch layer.
Bo Fu 0001 +3 more
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Multiwedgelets in Image Denoising
2013In this paper the definition of a multiwedgelet is introduced. The multiwedgelet is defined as a vector of wedgelets. In order to use a multiwedgelet in image approximation its visualization and computation methods are also proposed. The application of multiwedgelets in image denoising is presented, as well.
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Neural Adaptive Image Denoiser
2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2018We propose a novel neural network-based adaptive image denoiser, dubbased as Neural AIDE. Unlike other neural network-based denoisers, which typically apply supervised training to learn a mapping from a noisy patch to a clean patch, we formulate to train a neural network to learn context-based affine mappings that get applied to each noisy pixel.
Sungmin Cha, Taesup Moon
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