光学精密工程2026，Vol.34Issue(7)：1156-1169,14.DOI:10.37188/OPE.20263407.1156
用于物料混合均匀性检测的高光谱图像散焦模糊去除
Defocus deblur of hyperspectral image for material mixing uniformity detection
摘要
Abstract
Detection of material mixing uniformity is critical for enabling online quality monitoring and pro-cess optimization.This study addresses the degradation of uniformity evaluation caused by defocus blur in hyperspectral imaging(HSI).A physics-constrained self-supervised learning framework for unpaired hy-perspectral image deblurring(PC-SSL-HSI)is proposed.A Uformer-based architecture incorporating the SimAM attention mechanism is employed as the deblurring network,while adversarial training is intro-duced to align deblurred outputs with clear images in the feature space.In addition,a blur kernel predic-tion module is designed based on a classical degradation model to construct pseudo-sample pairs,enabling self-supervised learning that guides the network to emphasize local detail restoration in hyperspectral imag-es.Experimental results demonstrate that the proposed method effectively enhances image detail,suppress-es artifacts,and improves the accuracy of material mixing uniformity evaluation.On a simulated dataset,the peak signal-to-noise ratio(PSNR)reaches 34.970 and the structural similarity index(SSIM)reaches 0.900,with concentration prediction errors ranging from 0.022 8 to 0.031 2.Furthermore,hyperspectral imaging experiments for material mixing uniformity indicate that the proposed method outperforms compar-ative approaches in metrics such as Kullback-Leibler divergence and coefficient of variation,highlighting its strong potential for engineering applications.关键词
高光谱图像/去散焦模糊/混合均匀性/自监督学习/物理约束Key words
hyperspectral image/defocus deblur/mixing uniformity/self-supervised learning/physics-constrained分类
信息技术与安全科学引用本文复制引用
钱斐,胡凡,苟晓东,朱启兵..用于物料混合均匀性检测的高光谱图像散焦模糊去除[J].光学精密工程,2026,34(7):1156-1169,14.基金项目
国家自然科学基金资助项目(No.62273166) （No.62273166）
