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Image denoising and inpainting with deep neural networks
 In NIPS
, 2012
"... We present a novel approach to lowlevel vision problems that combines sparse coding and deep networks pretrained with denoising autoencoder (DA). We propose an alternative training scheme that successfully adapts DA, originally designed for unsupervised feature learning, to the tasks of image den ..."
Abstract

Cited by 14 (1 self)
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We present a novel approach to lowlevel vision problems that combines sparse coding and deep networks pretrained with denoising autoencoder (DA). We propose an alternative training scheme that successfully adapts DA, originally designed for unsupervised feature learning, to the tasks of image denoising and blind inpainting. Our method’s performance in the image denoising task is comparable to that of KSVD which is a widely used sparse coding technique. More importantly, in blind image inpainting task, the proposed method provides solutions to some complex problems that have not been tackled before. Specifically, we can automatically remove complex patterns like superimposed text from an image, rather than simple patterns like pixels missing at random. Moreover, the proposed method does not need the information regarding the region that requires inpainting to be given a priori. Experimental results demonstrate the effectiveness of the proposed method in the tasks of image denoising and blind inpainting. We also show that our new training scheme for DA is more effective and can improve the performance of unsupervised feature learning. 1
RESTORATION OF IMAGES CORRUPTED BY IMPULSE NOISE AND MIXED GAUSSIAN IMPULSE NOISE USING BLIND INPAINTING
, 1304
"... Abstract. This article studies the problem of image restoration of observed images corrupted by impulse noise and mixed Gaussian impulse noise. Since the pixels damaged by impulse noise contain no information about the true image, how to find this set correctly is a very important problem. We propos ..."
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Abstract. This article studies the problem of image restoration of observed images corrupted by impulse noise and mixed Gaussian impulse noise. Since the pixels damaged by impulse noise contain no information about the true image, how to find this set correctly is a very important problem. We propose two methods based on blind inpainting and ℓ0 minimization that can simultaneously find the damaged pixels and restore the image. By iteratively restoring the image and updating the set of damaged pixels, these methods have better performance than other methods, as shown in the experiments. In addition, we provide convergence analysis for these methods, these algorithms will converge to coordinatewise minimum points. In addition, they will converge to local minimum points (or with probability one) with some modifications in the algorithms.
IEEE SIGNAL PROCESSING LETTERS 1 NonLocal Euclidean Medians
"... Abstract—In this letter, we note that the denoising performance of NonLocal Means (NLM) can be improved at large noise levels by replacing the mean by the Euclidean median. We call this new denoising algorithm the NonLocal Euclidean Medians (NLEM). At the heart of NLEM is the observation that the ..."
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Abstract—In this letter, we note that the denoising performance of NonLocal Means (NLM) can be improved at large noise levels by replacing the mean by the Euclidean median. We call this new denoising algorithm the NonLocal Euclidean Medians (NLEM). At the heart of NLEM is the observation that the median is more robust to outliers than the mean. In particular, we provide a simple geometric insight that explains why NLEM performs better than NLM in the vicinity of edges, particularly at large noise levels. NLEM can be efficiently implemented using iteratively reweighted least squares, and its computational complexity is comparable to that of NLM. We provide some preliminary results to study the proposed algorithm and to compare it with NLM. Index Terms—Image denoising, nonlocal means, Euclidean median, iteratively reweighted least squares (IRLS), Weiszfeld algorithm. I.
Research Statement
"... My research is in the area of differential geometry, centering around various applications of integrable systems to submanifold geometries. I will first explain the overall goal of this very interdisciplinary and active field, then summarize my contributions and some ongoing and future projects. 1 ..."
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My research is in the area of differential geometry, centering around various applications of integrable systems to submanifold geometries. I will first explain the overall goal of this very interdisciplinary and active field, then summarize my contributions and some ongoing and future projects. 1