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Title:
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IMAGE BLOCK COMPRESSED SENSING UNDER LOW SAMPLING-RATIO |
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Author(s):
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Zhengguang Xie, Huang Hongwei, Cai Xu |
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ISBN:
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978-989-8533-38-8 |
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Editors:
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Katherine Blashki and Yingcai Xiao |
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Year:
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2015 |
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Edition:
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Single |
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Keywords:
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Image reconstruction; Block compressed sensing; Total variation; Overlapped sampling; Adaptive sampling-ratio assignation |
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Type:
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Full Paper |
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First Page:
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227 |
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Last Page:
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234 |
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Language:
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English |
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Cover:
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Full Contents:
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click to dowload 
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Paper Abstract:
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Block Compressed Sensing (BCS) is a new image sampling/compressing method with compressed sensing (CS). To solve the performance degradation of BCS-SPL (BCS with Smoothed Projected Landweber algorithm) at low sampling-ratio, we propose a novel algorithm called Total Variation based Adaptive-Sampling BCS with OMP (TVAS-BCS-OMP). TVAS-BCS-OMP blocks the whole image in an overlapping way to eliminate blocking effect. It assigns sampling-ratio depending on each block texture complexity, which is measured by the blocks Total Variation (TV) so that the blocks with big TV can attain higher sampling-ratio. Then only limited nonzero coefficients in each block are retained according to the adaptively assigned sampling-ratio. At last, we sample the blocks and conducts OMP reconstruction respectively. The experimental results show that under the condition of low initial sampling-ratio (lower than 0.2), TVAS-BCS-OMP achieves better reconstruction precision than BCS-SPL, especially in the blocks with complex texture. In addition, the new algorithm costs shorter reconstruction time than BCS-SPL algorithm. |
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