中圖分類號： TP391.7
文獻(xiàn)標(biāo)識碼： A
DOI： 10.19358/j.issn.2096-5133.2022.05.006
引用格式： 竺可沁，林珊玲，林志賢，等. 基于多層次特征提取的輕量級超分辨率重建算法[J].信息技術(shù)與網(wǎng)絡(luò)安全，2022，41(5)：38-44.

A lightweight super-resolution algorithm based on multi-level feature extraction

Abstract： In order to solve the problem that today′s algorithms based on deep learning have simple feature extraction, complex structure and huge parameters, a lightweight super-resolution algorithm based on multi-layer feature extraction is proposed. The algorithm adopts the multi-level feature extraction method. Firstly, the low feature of the image is extracted. Secondly, the deep feature extraction module containing multiple parallel convolutions is used to extract the deep feature of the image. A multi-scale feature fusion and reconstruction module with learning weights is designed to make full use of the extracted multi-level information to reconstruct images. Experimental results show that the peak signal noise ratio and structural similarity of reconstructed images are better than the current algorithms in most cases. Compared with the comparison algorithm, the number of parameters and operation time remain ahead, which proves the lightweight feature of the network.

Key words : super-resolution reconstruction；multi-level feature extraction；multi-scale feature fusion；convolutional neural network；image enhancement