On the simplification of multi-focus image fusion using dictionary-based sparse representation
碩士 === 國立中興大學 === 電機工程學系所 === 107 === This paper proposes a fast implementation for multi-focus image fusion using dictionary-based sparse representation. The proposed method reduces the computation complexity of the existing method by synthesizing feature signals from the trained sparse coefficient...
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| Format: | Others |
| Language: | en_US |
| Published: |
2019
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| Online Access: | http://ndltd.ncl.edu.tw/cgi-bin/gs32/gsweb.cgi/login?o=dnclcdr&s=id=%22107NCHU5441025%22.&searchmode=basic |
| Summary: | 碩士 === 國立中興大學 === 電機工程學系所 === 107 === This paper proposes a fast implementation for multi-focus image fusion using dictionary-based sparse representation. The proposed method reduces the computation complexity of the existing method by synthesizing feature signals from the trained sparse coefficient feature vectors in order to classify each pixel in a source image as focused or defocused, Since the computation of the OMP algorithm for sparse coefficient the exist method complexity of the proposed method can be only 1/100 of [1]. Simulation results further demonstrate that the fused image of the proposed method has the same quality as that of the existing method.
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