Journal ArticleDOI
DCT Regularized Extreme Visual Recovery
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TLDR
A novel DCT regularizer is proposed that involves all pixels and produces smooth estimations in any view and is superior to the state-of-the-art methods in terms of peak signal-to-noise ratio and structural similarity.Abstract:
Here we study the extreme visual recovery problem, in which over 90% of pixel values in a given image are missing. Existing low rank-based algorithms are only effective for recovering data with at most 90% missing values. Thus, we exploit visual data’s smoothness property to help solve this challenging extreme visual recovery problem. Based on the discrete cosine transform (DCT), we propose a novel DCT regularizer that involves all pixels and produces smooth estimations in any view. Our theoretical analysis shows that the total variation regularizer, which only achieves local smoothness, is a special case of the proposed DCT regularizer. We also develop a new visual recovery algorithm by minimizing the DCT regularizer and nuclear norm to achieve a more visually pleasing estimation. Experimental results on a benchmark image data set demonstrate that the proposed approach is superior to the state-of-the-art methods in terms of peak signal-to-noise ratio and structural similarity.read more
Citations
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Tensor Factorization for Low-Rank Tensor Completion
TL;DR: A novel low-rank tensor factorization method for efficiently solving the 3-way tensor completion problem and it is proved that the proposed alternating minimization algorithm can converge to a Karush–Kuhn–Tucker point.
Proceedings ArticleDOI
Deep Adversarial Subspace Clustering
Pan Zhou,Yunqing Hou,Jiashi Feng +2 more
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Reversible data hiding in encrypted image with separable capability and high embedding capacity
TL;DR: Experimental results demonstrate that, the proposed high-capacity reversible data hiding scheme can generally achieve better performances, including higher embedding rate and better visual quality of the direct-decrypted image and the final recovered image, than some of the state-of-the-art schemes.
Journal ArticleDOI
Occluded face recognition using low-rank regression with generalized gradient direction
Cho-Ying Wu,Jian-Jiun Ding +1 more
TL;DR: Wang et al. as discussed by the authors proposed a gradient direction-based hierarchical adaptive sparse and low-rank (GD-HASLR) model to solve the real-world occluded face recognition problem.
Journal ArticleDOI
Robust Subspace Segmentation by Self-Representation Constrained Low-Rank Representation
TL;DR: This paper proposes a new LRR-related algorithm, termed self- representation constrained low-rank presentation (SRLRR), which contains a self-representation constraint which is used to compel the obtained coefficient matrices can be reconstructed by themselves.
References
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Distinctive Image Features from Scale-Invariant Keypoints
TL;DR: This paper presents a method for extracting distinctive invariant features from images that can be used to perform reliable matching between different views of an object or scene and can robustly identify objects among clutter and occlusion while achieving near real-time performance.
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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.
Distinctive Image Features from Scale-Invariant Keypoints
TL;DR: The Scale-Invariant Feature Transform (or SIFT) algorithm is a highly robust method to extract and consequently match distinctive invariant features from images that can then be used to reliably match objects in diering images.