中圖分類號(hào)：TP391
文獻(xiàn)標(biāo)識(shí)碼：A
DOI： 10.19358/j.issn.2096-5133.2020.03.005
引用格式：伏伶麗,，任超,，何小海,等基于稀疏編碼和各向異性濾波的超分辨率重建［J］.信息技術(shù)與網(wǎng)絡(luò)安全，2020,39（3）：23-28.

Super-esolution reconstruction based on sparse coding and anisotropic filtering

Abstract： To solve the problem of poor edgepreserving performance in the nonlocal sparsebased superresolution reconstruction method,the algorithm based on sparse coding and anisotropic filtering is proposed.Specifically,，an anisotropic feature model is trained by the convolution neural network and anisotropic guided filtering,and then a local structure prior is constructed by using the feature model to complement the nonlocal sparse prior,and thus improves the edge preserving ability of the algorithm.The trained model of this algorithm is tested with the general test data.The experimental results demonstrate that the proposed algorithm effectively improves the edgepreserving performance,and obtains higher objective evaluation indices and images with better visual effect.

Key words : sparse coding;anisotropic guided filtering;super-resolution reconstruction;convolutional neural network