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.
MIN Lihua1*(闵莉花), FENG Can2 (冯 灿)
. Compressive Sensing Reconstruction Based on Weighted Directional Total Variation[J]. Journal of Shanghai Jiaotong University(Science), 2017
, 22(1)
: 114
-120
.
DOI: 10.1007/s12204-017-1809-5
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