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Group Sparsity Residual Constraint With Non-Local Priors for Image Restoration
IEEE Transactions on Image Processing, 2020Group sparse representation (GSR) has made great strides in image restoration producing superior performance, realized through employing a powerful mechanism to integrate the local sparsity and nonlocal self-similarity of images.
Zhiyuan Zha +4 more
semanticscholar +1 more source
2012
This is the first book devoted to the systematic study of sparse graphs and sparse finite structures. Although the notion of sparsity appears in various contexts and is a typical example of a fuzzy notion, the authors devised an unifying classification of general classes of structures. This approach is very robust and it has many remarkable properties.
Nesetril, Jaroslav +1 more
openaire +2 more sources
This is the first book devoted to the systematic study of sparse graphs and sparse finite structures. Although the notion of sparsity appears in various contexts and is a typical example of a fuzzy notion, the authors devised an unifying classification of general classes of structures. This approach is very robust and it has many remarkable properties.
Nesetril, Jaroslav +1 more
openaire +2 more sources
Efficient and Effective Sparse LSTM on FPGA with Bank-Balanced Sparsity
Symposium on Field Programmable Gate Arrays, 2019Neural networks based on Long Short-Term Memory (LSTM) are widely deployed in latency-sensitive language and speech applications. To speed up LSTM inference, previous research proposes weight pruning techniques to reduce computational cost. Unfortunately,
Shijie Cao +8 more
semanticscholar +1 more source
2019
<p>In this dissertation, we explore and illustrate the concept of ranked sparsity, a phenomenon that often occurs naturally in the presence of derived variables. Ranked sparsity arises in modeling applications when an expected disparity exists in the quality of information between different feature sets.
Ryan Andrew Peterson +6 more
openaire +1 more source
<p>In this dissertation, we explore and illustrate the concept of ranked sparsity, a phenomenon that often occurs naturally in the presence of derived variables. Ranked sparsity arises in modeling applications when an expected disparity exists in the quality of information between different feature sets.
Ryan Andrew Peterson +6 more
openaire +1 more source
2011
Today, sparsity techniques have been widely used to address practical problems in the fields of medical imaging, machine learning, computer vision, data mining, compressive sensing, image processing, video analysis and multimedia. We will briefly introduce the related sparsity techniques and their successful applications on compressive sensing, sparse
openaire +1 more source
Today, sparsity techniques have been widely used to address practical problems in the fields of medical imaging, machine learning, computer vision, data mining, compressive sensing, image processing, video analysis and multimedia. We will briefly introduce the related sparsity techniques and their successful applications on compressive sensing, sparse
openaire +1 more source
Superresolution via Sparsity Constraints
SIAM Journal on Mathematical Analysis, 1992The problem of recovering a measure \(\mu\) supported on a lattice of span \(\Delta\) from measurements \(\hat\mu(\omega)\) at frequencies \(|\omega|\leq\Omega\) is considered. The author shows that even if \(\Omega\) is much smaller than the Nyquist frequency \(\pi/\Delta\) and the measurements are noisy, stable recovery is possible if the measure ...
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A systematic literature review of sparsity issues in recommender systems
Social Network Analysis and Mining, 2020Nouhaila Idrissi, A. Zellou
semanticscholar +1 more source