计算机与数字工程2024，Vol.52Issue(4)：1110-1114,5.DOI:10.3969/j.issn.1672-9722.2024.04.026
基于重参数化的超分辨率重建
Super-resolution Reconstruction Based on Re-parameterization
摘要
Abstract
In view of the contradiction between the speed and accuracy of the existing single image super-resolution(SISR)model,this paper presents a lightweight re-parameterization model for image realization reconstruction.The model is trained to en-sure accuracy by using a model with a more complex structure,and the model is equivalently transformed into a simple convolution to improve the speed during inference.At the same time,the addition of a multi-supervisory structure makes the model converge faster and more flexible.The quality and efficiency of the reconstruction model are evaluated by the peak signal-to-noise ratio and structural similarity.It is verified that the proposed model has the advantages of light weight and good reconstruction quality in the existing super-resolution reconstruction methods.关键词
单图像超分辨率/卷积神经网络/多监督学习/重参数化Key words
single image super-resolution/convolutional neural network/multi-supervised learning/re-parameterization分类
信息技术与安全科学引用本文复制引用
田蕾,申艺..基于重参数化的超分辨率重建[J].计算机与数字工程,2024,52(4):1110-1114,5.基金项目
国家自然科学基金项目(编号:61803199,U2033201)资助. （编号:61803199,U2033201）
