Single Image Super-Resolution Based on the Feature Sign Method

Q4 Engineering
Xiaofeng Li, L. Zeng, Jin Xu, Shiping Ma
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引用次数: 4

Abstract

Recently, the super-resolution methods based on sparse representation has became a research hotpot in signal processing. How to calculate the sparse coefficients fast and accurately is the key of sparse representation algorithm. In this paper, we propose a feature sign method to compute the sparse coefficients in the search step. Inspired by the compressed sensing theory, two dictionaries are jointly learnt to conduct super-resolution in this method. The feature sign algorithm changes the non-convex problem to a convex one by guessing the sign of the sparse coefficient at each iteration. It improves the accuracy of the obtained sparse coefficients and speeds the algorithm. Simulation results show that the proposed scheme outperforms the interpolation methods and classic sparse representation algorithms in both subjective inspects and quantitative evaluations.
基于特征符号方法的单幅图像超分辨率
近年来,基于稀疏表示的超分辨方法已成为信号处理领域的研究热点。如何快速准确地计算稀疏系数是稀疏表示算法的关键。在本文中,我们提出了一种特征符号方法来计算搜索步骤中的稀疏系数。该方法受压缩感知理论的启发,联合学习两个字典进行超分辨。特征符号算法通过在每次迭代中猜测稀疏系数的符号,将非凸问题转化为凸问题。它提高了得到的稀疏系数的准确性,加快了算法的速度。仿真结果表明,该方法在主观评价和定量评价方面都优于插值方法和经典稀疏表示算法。
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来源期刊
电子科技大学学报
电子科技大学学报 Engineering-Electrical and Electronic Engineering
CiteScore
1.40
自引率
0.00%
发文量
7228
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