Improved BP neural network for image super-resolution

被引：1
|
作者：
Zhu, Fu-Zhen [1 ,3 ]
Zhu, Bing [2 ]
Li, Pei-Hua [3 ]
Ding, Qun [3 ]
机构：
[1] Electronic Science and Technology Post-Doctoral Research Station, Heilongjiang University, Harbin 150040, China
[2] Colledge of Electronics and Information Engineering, Harbin Institute of Technology, Harbin 150001, China
[3] Colledge of Electronic Engineering, Heilongjiang University, Harbin 150040, China
来源：
Xi Tong Gong Cheng Yu Dian Zi Ji Shu/Systems Engineering and Electronics | 2014年 / 36卷 / 06期
关键词：
Neural networks - Optical resolving power - Problem solving - Sampling - Backpropagation;
D O I：
10.3969/j.issn.1001-506X.2014.06.31
中图分类号：
学科分类号：
摘要：
To solve the problem of block traces in the super-resolution results, an improved back propagation (BP) neural network (BPNN) for super-resolution reconstruction (SRR) is established to further improve SRR image quality. Two important problems which directly affect super-resolution results are solved. First, the problem of BPNN training samples is solved. The mapping mode of 8×8 → 16×16 is improved as 2×2 → 4×4, at the same time, orders of training samples construction are optimized in a mode of one pixel interval. Second, the problem of speeding up net training convergence is solved. The net training rule is improved from BP algorithm to the improved scaled conjugate gradient algorithm. Experiment results show that the improved method increases the quantity of training samples, enhances the SRR quality of BPNN output results images, and effectively solves the block traces problem of SRR results. The peak signal noise ratio of the SRR image increases about 8 dB.
引用
收藏
页码：1215 / 1219
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