Hyperspectral image reconstruction based on the fusion of diffracted rotation blurred and clear images. (January 2023)

• Record Type: Journal Article
• Authors: Xu, Hao; Hu, Haiquan; Chen, Shiqi; Xu, Zhihai; Li, Qi; Jiang, Tingting; Chen, Yueting
• Is Part Of: Optics and lasers in engineering. Volume 160(2023)
• Publication Date: 2023-01
• Subjects: Hyperspectral image reconstruction -- Hyperspectral imaging -- Diffraction -- Image fusion -- Convolutional neural network; Lasers in engineering -- Periodicals; Optical measurements -- Periodicals; Optics -- Periodicals; Lasers en ingénierie -- Périodiques; Mesures optiques -- Périodiques; Optique -- Périodiques; 621.36605
• DOI: 10.1016/j.optlaseng.2022.107274
• Languages: English
• ISSNs: 0143-8166

Abstract

Highlights: We propose a joint diffractive imaging and clear imaging system that can provide hyperspectral images with high spatial resolution and spectral precision by fusing and reconstructing the images captured by the two imaging branches. We propose a feature extraction block (FEB) and a double residual block (DRB) based on the two-branch network framework, which effectively improve the network's capability to reconstruct hyperspectral images. Extensive experiments show that our method outperforms other state-of-the-art approaches in terms of PSNR, SSIM, and SAM metrics. Abstract: To overcome the problems of imaging speed and bulky volume of the traditional hyperspectral imaging systems, the recently proposed compact, snapshot hyperspectral imaging system with diffracted rotation has attracted a lot of interest. Due to the severe degradation of the diffracted rotation blurred image, the restored hyperspectral image (HSI) suffers from a lack of spatial detail information and spectral accuracy. To improve the quality of the reconstructed HSI, we present a joint imaging system of diffractive imaging and clear imaging as well as a convolutional neural network (CNN) based method with two input branches for HSI reconstruction. In the reconstruction network, we develop a feature extraction block (FEB) to extract the features of the two input images, respectively. Subsequently, a double residual block (DRB) is designed to fuse and reconstruct the extracted features. Experimental results show that HSI with high spatial resolution and spectral accuracy can be reconstructed. Our method outperforms the state-of-the-art methods in terms of quantitative metrics and visual quality.