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Single-image blind deconvolution is a challenging illposed inverse problem which requires regularization techniques to stabilize the restoration process. Its purpose is to recover an underlying blur kernel and a latent image from only one blurred image. In most imaging situations, the blur kernel is not only spatially sparse, but also piecewise smooth with the support of a continuous curve. Thus this...
GPU has recently gained considerable attention in getting significant performance, for application raging from scientific computing to database sorting and search. General-purpose computing on GPU can easily reduce the execution time but results in an associated increase in the energy consumption. This paper analyzes energy consumption of parallel algorithms executing on GPU and provide a methodology...
Edge detection is arguably the most important operation in low level computer vision. Mean shift is an effective iterative algorithm widely used in edge detection. But the cost of computation prohibits Mean shift algorithm for high dimensions feature space. In this paper, a fast adaptive mean shift algorithm is proposed for edge detection. It makes use of one approximate nearest neighbors search method,...
This paper presents a new access-density-based prefetching strategy to improve prefetching for the access patterns that have not been dealt with in the current Linux read-ahead algorithm. These access patterns include: reading file data backwards, reading files in a stridden way (leaving holes in between two adjacent references), alternating references between multiple file regions and reading files...
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