红外技术2024，Vol.46Issue(5)：501-509,9.
基于轻量化多尺度下采样网络的红外图像非均匀性校正算法
Infrared Image Non-uniformity Correction Algorithm Based on Lightweight Multiscale Downsampling Network
摘要
Abstract
Infrared imaging systems often produce fringe noise in imaging results owing to the non-uniformity of the detection unit.To obtain better correction results,most deep learning-based infrared image non-uniformity correction algorithms adopt complex network structures,which increase the computational cost.This study proposes a lightweight network-based infrared image non-uniformity correction algorithm and designs a lightweight multi-scale downsampling module(LMDM)for the encoding process of the Unet network.The LMDM uses pixel splitting and channel reconstruction to realize feature map downsampling and realizes multi-scale feature extraction using multiple cascaded depth-wise separable convolutions(DSC).In addition,the algorithm introduces a lightweight channel attention mechanism for adjusting feature weights to achieve better contextual information fusion.The experimental results show that the proposed algorithm reduces memory use by more than 70%and improves the processing speed of the infrared images by more than 24%compared with the comparison algorithm while ensuring that the corrected image has a clear texture,rich details,and sharp edges.关键词
红外图像/非均匀性校正/深度学习/轻量化/多尺度特征提取Key words
infrared image/non-uniformity correction/deep learning/lightweight/multi-scale feature extraction分类
计算机与自动化引用本文复制引用
牟新刚,朱太龙,周晓..基于轻量化多尺度下采样网络的红外图像非均匀性校正算法[J].红外技术,2024,46(5):501-509,9.基金项目
国家自然科学基金项目(61701357) （61701357）
中央高校基本科研业务费专项资金资助(183204007). （183204007）
