Journal ArticleDOI
A tensor-based nonlocal total variation model for multi-channel image recovery
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TLDR
Extensive experimental results demonstrate that the proposed regularizer is systematically superior over other competing local and nonlocal regularization approaches, both quantitatively and visually.About:
This article is published in Signal Processing.The article was published on 2018-12-01. It has received 6 citations till now. The article focuses on the topics: Image gradient & Image processing.read more
Citations
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Journal ArticleDOI
An efficient nonlocal variational method with application to underwater image restoration
TL;DR: Both the real underwater image application test and the simulation experiment demonstrate that the proposed underwater nonlocal total variational (UNLTV) approach achieves superb performance on dehazing, denoising, and improving the visibility of underwater images.
Journal ArticleDOI
Directional fractional-order total variation hybrid regularization for image deblurring
Qiaohong Liu,Song Gao +1 more
TL;DR: Numerical experiments demonstrate that the proposed method can protect the texture and edge while eliminating the staircase effect effectively, and the split Bregman algorithm and fast Fourier transform theory are utilized to optimize the proposed hybrid regularization model.
Journal ArticleDOI
Nonblind Image Deblurring Based on Bi-Composition Decomposition by Local Smoothness and Nonlocal Self-Similarity Priors
TL;DR: The split Bregman-based iteration algorithm and four-directional fast gradient projection algorithm are introduced to optimize the proposed $L_{1}$ -regularized problem and demonstrate the efficiency and viability of the proposed method for preserving salient edges and texture details while alleviating the artifacts.
Posted Content
Spatial-Spectral Manifold Embedding of Hyperspectral Data
Danfeng Hong,Danfeng Hong,Jing Yao,Jing Yao,Jing Yao,Xin Wu,Jocelyn Chanussot,Xiao Xiang Zhu,Xiao Xiang Zhu +8 more
TL;DR: A novel hyperspectral embedding approach by simultaneously considering spatial and spectral information, called spatial-spectral manifold embedding (SSME), which not only learns the spectral embedding by using the adjacency matrix obtained by similarity measurement between spectral signatures, but also models the spatial neighbours of a target pixel in hyperspectrals by sharing the same weights (or edges) in the process of learning embedding.
Journal ArticleDOI
Hyperspectral Fusion Using Weighted Nonlocal Vector Total Variation
TL;DR: In this article , a non-local weighted total-variation regularizer was proposed to exploit the prior of textured and repetitive structures of multispectral images, where the weights are derived from the MS image and pixel variations over nonlocal neighborhoods are considered.
References
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Proceedings Article
ImageNet Classification with Deep Convolutional Neural Networks
TL;DR: The state-of-the-art performance of CNNs was achieved by Deep Convolutional Neural Networks (DCNNs) as discussed by the authors, which consists of five convolutional layers, some of which are followed by max-pooling layers, and three fully-connected layers with a final 1000-way softmax.
Journal ArticleDOI
Image quality assessment: from error visibility to structural similarity
TL;DR: In this article, a structural similarity index is proposed for image quality assessment based on the degradation of structural information, which can be applied to both subjective ratings and objective methods on a database of images compressed with JPEG and JPEG2000.
Book
Distributed Optimization and Statistical Learning Via the Alternating Direction Method of Multipliers
TL;DR: It is argued that the alternating direction method of multipliers is well suited to distributed convex optimization, and in particular to large-scale problems arising in statistics, machine learning, and related areas.
Journal ArticleDOI
Nonlinear total variation based noise removal algorithms
TL;DR: In this article, a constrained optimization type of numerical algorithm for removing noise from images is presented, where the total variation of the image is minimized subject to constraints involving the statistics of the noise.
Journal ArticleDOI
A Singular Value Thresholding Algorithm for Matrix Completion
TL;DR: This paper develops a simple first-order and easy-to-implement algorithm that is extremely efficient at addressing problems in which the optimal solution has low rank, and develops a framework in which one can understand these algorithms in terms of well-known Lagrange multiplier algorithms.
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Zhili Yang,Mathews Jacob +1 more