简化退化模型的真实图像超分辨率网络  

Real image super-resolution network for simplifying the degradation model

作　　者：林旭锋 吴丽君[1] Lin Xufeng;Wu Lijun(College of Physics and Information Engineering,Fuzhou University,Fuzhou 350108,China)

机构地区：[1]福州大学物理与信息工程学院,福建福州350108

出　　处：《网络安全与数据治理》2024年第3期34-39,共6页CYBER SECURITY AND DATA GOVERNANCE

基　　金：国家自然科学基金项目(62271151);福建省自然科学基金项目(2021J01580)。

摘　　要：图像超分辨率任务常用双三次下采样以构造数据集训练网络,但双三次下采样由于退化模型固定,导致网络泛化能力低,无法用于真实世界低分辨率图像。为解决上述问题本文提出预处理模块,通过预处理模块与双三次下采样数据集得到的网络相结合,在减少资源消耗的同时提高其泛化能力。此外,还针对不同的精度需求设计了特征学习训练策略和多任务联调策略。通过根据不同需求采用相应的训练策略,在满足精度需求的同时具有消耗计算资源少、训练速度快以及适用范围广的特点。实验证明,增加预处理模块的网络以较少的模型参数增加量换取了重建效果和感知质量方面的较大提升,并且通过不同策略实现了进一步的精度提高。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.

关 键 词：超分辨率 预处理模块 多任务学习 计算机视觉 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]