中国光学（中英文）2024，Vol.17Issue(6)：1316-1328,13.DOI:10.37188/CO.2023-0196
基于注意力残差网络的快照式多光谱相机图像重构
Image reconstruction of snapshot multispectral camera based on an attention residual network
摘要
Abstract
With the rapid advancement of spectral imaging technology,the use of multispectral filter array(MSFA)to collect the spatial and spectral information of multispectral images has become a research hotspot.The uses of the original data are limited because of its low sampling rate and strong spectral inter-correlation for reconstruction.Therefore,we propose a multi-branch attention residual network model for spatial-spec-tral association based on an 8-band 4x4 MSFA with all-pass bands.First,the multi-branch model was used to learn the image features after interpolation in each band;second,the feature information of the eight bands and the all-pass band were united by the spatial channel attention model designed in this paper,and the ap-plication of multi-layer convolution and the convolutional attention module and the use of residual compens-ation effectively compensated the color difference of each band and enriched the edge texture-related feature information.Finally,the preliminary interpolated full-pass band and the rest of the band feature information were used for feature learning of the spatial and spectral correlations of multispectral images through resid-ual dense blocks without batch normalization to match the spectral information of each band.Experimental results show that the peak signal-to-noise ratio,structural similarity,and spectral angular similarity of the test image under the D65 light source outperform the state-of-the-art deep learning method by 3.46％,0.27％,and 6％,respectively.This method not only reduces artifacts but also obtains more texture details.关键词
多光谱滤光片阵列/图像重构/空谱联合/残差网络/深度学习Key words
multispectral filter array/image reconstruction/spatial-spectral combination/residual network/deep learning分类
信息技术与安全科学引用本文复制引用
闫纲琦,梁宗林,宋延嵩,董科研,张博,刘天赐,张雷,王岩柏..基于注意力残差网络的快照式多光谱相机图像重构[J].中国光学（中英文）,2024,17(6):1316-1328,13.基金项目
国家重点研发计划项目(No.2022YFB3902500,No.2021YFA0718804) （No.2022YFB3902500,No.2021YFA0718804）
国家自然科学基金青年基金(No.62305032)Supported by National Key R&D Program(No.2022YFB3902500,No.2021YFA0718804) （No.62305032）
Youth Founda-tion of National Natural Science Foundation of China(No.62305032) （No.62305032）
