基于注意力残差网络的快照式多光谱相机图像重构OA北大核心CSTPCD

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.