中圖分類(lèi)號(hào)：TP391文獻(xiàn)標(biāo)識(shí)碼：ADOI:10.19358/j.issn.2097-1788.2024.03.006
引用格式：林旭鋒，吳麗君.簡(jiǎn)化退化模型的真實(shí)圖像超分辨率網(wǎng)絡(luò)［J］.網(wǎng)絡(luò)安全與數(shù)據(jù)治理，2024，43（3）：34-39.

Real image super resolution network for simplifying the degradation model

Abstract： In the task of image super resolution, bicubic down sampling is commonly used to construct datasets for training networks. However, due to the fixed degradation model, bicubic down sampling results in low generalization ability of the network and cannot be used for real world low resolution images. To address this problem, this paper proposes a preprocessing module that combines with the network obtained from the bicubic down sampling dataset to improve its generalization ability while reducing resource consumption. In addition, this paper also designs feature learning training strategies and multi task joint training strategies for different accuracy requirements. By adopting corresponding training strategies according to different requirements, it can meet the accuracy requirements while having the characteristics of low computational resource consumption, fast training speed, and wide applicability. Experiments have shown that adding a network with a preprocessing module can achieve greater improvements in reconstruction effect and perceptual quality with less model parameter increase, and further improve accuracy through different strategies.

Key words : super resolution; preprocessing module; multi task learning; computer vision