Compact Self-adaptive Coding for Spectral Compressive Sensing

Zhan Shi, Hao Ye, Tao Lv, Yibo Wang, Xun Cao

Abstract: Spectral snapshot compressive imaging (SCI) has been extensively studied and applied to various fields. Although the typical coded aperture snapshot spectral imaging (CASSI) presents an effective paradigm, its fixed code designs do not sufficiently exploit flexible and optimal modulation in consideration of scene sparsity. In this paper, we present a novel Compact Self-adaptive optical Coding framework for Spectral Compressive Sensing, termed 3CS, to optimize the coded pattern adaptively for better hyper-spectral videos perception. Our framework enables extracting context high-frequency components from the compressed domain without requiring hybrid guiding camera. The specifically designed mask distribution enables higher light efficiency, and is robust against temporal correlation reduction when processing dynamic spectral videos. Extensive experiments and model discussions validate the superiority of the proposed framework over traditional end-to-end (E2E) methods in various aspects for the spectral reconstruction.