Improvement of imaging quality of SPIDER by dictionary learning

Lei Wu; He Yuan; Xiangchao Zhang; Xinyue Liu; Haoran Meng

doi:10.1117/12.2611729

8 July 2022 Improvement of imaging quality of SPIDER by dictionary learning

Lei Wu, He Yuan, Xiangchao Zhang, Xinyue Liu, Haoran Meng

Proceedings Volume 12281, 2021 International Conference on Optical Instruments and Technology: Optoelectronic Imaging/Spectroscopy and Signal Processing Technology; 1228102 (2022) https://doi.org/10.1117/12.2611729
Event: 2021 International Conference on Optical Instruments and Technology, 2022, Online Only

Abstract

Sparse sampling of spectral components in Segmented Planar Imaging Detector for Electro-Optical Reconnaissance is an essential limiting factor to the imaging resolution. A dictionary learning method is proposed to improve the imaging quality. The images are segmented into patches, and data are extracted directly from small patches and taken as dictionary elements. By training high-and-low resolution image pairs, a coupled dictionary is obtained. The TV/L1 minimization and alternating direction multiplier method are used to restore high-resolution images. In this way, the quality metric RMSE of images is improved from 20.99 to 14.99, and PSNR from 21.69 dB to 24.62 dB.

Citation Download Citation

Lei Wu, He Yuan, Xiangchao Zhang, Xinyue Liu, and Haoran Meng "Improvement of imaging quality of SPIDER by dictionary learning", Proc. SPIE 12281, 2021 International Conference on Optical Instruments and Technology: Optoelectronic Imaging/Spectroscopy and Signal Processing Technology, 1228102 (8 July 2022); https://doi.org/10.1117/12.2611729

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