Journal ArticleDOI
Iterative gradient projection algorithm for two-dimensional compressive sensing sparse image reconstruction
Gao Chen,Defang Li,Jiashu Zhang +2 more
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TLDR
A 2D CS sparse image reconstruction algorithm based on iterative gradient projection is proposed that is more efficient and robust, not only yielding higher peak-signal-to-noise ratio but also reconstructing images of better subjective visual quality.About:
This article is published in Signal Processing.The article was published on 2014-11-01. It has received 40 citations till now. The article focuses on the topics: Iterative reconstruction & Reconstruction algorithm.read more
Citations
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Hiding cipher-images generated by 2-D compressive sensing with a multi-embedding strategy
TL;DR: A novel color image encryption scheme to generate visually meaningful cipher image that enhances the relationship between plain image and encryption process and Embedding hash value into carrier image prevents extra transmission and storage is proposed.
Journal ArticleDOI
Robust Coding of Encrypted Images via 2D Compressed Sensing
Bo Zhang,Di Xiao,Yong Xiang +2 more
TL;DR: A novel 2D CS (2 DCS) based ETC (2DCS-ETC) scheme that can simultaneously achieve high security and low computational complexity with better robustness is proposed.
Journal ArticleDOI
A visually secure image encryption scheme based on 2D compressive sensing and integer wavelet transform embedding
TL;DR: A visually secure image encryption scheme based on two-dimensional compressive sensing and integer wavelet transform (IWT) embedding that can ensure data security and increase the speed of the decryption algorithm, and the IWT embedding can achieve visual security without loss of information is proposed.
Journal ArticleDOI
An efficient algorithm for designing projection matrix in compressive sensing based on alternating optimization
TL;DR: Simulations with synthetic data and real images demonstrate that the obtained projection matrix significantly improves the signal recovery accuracy of a system and outperforms those obtained by the existing algorithms.
Journal ArticleDOI
Hierarchical Visual Perception and Two-Dimensional Compressive Sensing for Effective Content-Based Color Image Retrieval
TL;DR: The proposed method has demonstrated much improved retrieval accuracy, especially for images with rich color contents and detail, yet the computational complexity has been significantly reduced to meet the needs for real-time online applications.
References
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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.
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A theory for multiresolution signal decomposition: the wavelet representation
TL;DR: In this paper, it is shown that the difference of information between the approximation of a signal at the resolutions 2/sup j+1/ and 2 /sup j/ (where j is an integer) can be extracted by decomposing this signal on a wavelet orthonormal basis of L/sup 2/(R/sup n/), the vector space of measurable, square-integrable n-dimensional functions.
Book
Compressed sensing
TL;DR: It is possible to design n=O(Nlog(m)) nonadaptive measurements allowing reconstruction with accuracy comparable to that attainable with direct knowledge of the N most important coefficients, and a good approximation to those N important coefficients is extracted from the n measurements by solving a linear program-Basis Pursuit in signal processing.
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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
Atomic Decomposition by Basis Pursuit
TL;DR: Basis Pursuit (BP) is a principle for decomposing a signal into an "optimal" superposition of dictionary elements, where optimal means having the smallest l1 norm of coefficients among all such decompositions.