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OA北大核心CSTPCD

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.