Abstract
Directionality of image plays a very important role in human visual system and it is important prior information of image. In this paper we propose a weighted directional total variation model to reconstruct image from its finite number of noisy compressive samples. A novel self-adaption, texture preservation method is designed to select the weight. Inspired by majorization-minimization scheme, we develop an efficient algorithm to seek the optimal solution of the proposed model by minimizing a sequence of quadratic surrogate penalties. The numerical examples are performed to compare its performance with four state-of-the-art algorithms. Experimental results clearly show that our method has better reconstruction accuracy on texture images than the existing scheme.
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Foundation item: the National Natural Science Foundation of China (Nos. 11401318 and 11671004), the Natural Science Foundation of the Jiangsu Higher Education Institutions of China (No. 15KJB110018) and the Scientific Research Foundation of NUPT (No. NY214023)
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Min, L., Feng, C. Compressive sensing reconstruction based on weighted directional total variation. J. Shanghai Jiaotong Univ. (Sci.) 22, 114–120 (2017). https://doi.org/10.1007/s12204-017-1809-5
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DOI: https://doi.org/10.1007/s12204-017-1809-5