Provable deep video denoiser using spatial-temporal information for video snapshot compressive imaging: Algorithm and convergence analysis

Baoshun Shi, Dan Li, Yuxin Wang, Yueming Su, Qiusheng Lian

Abstract: Highlights•We propose a provable and trainable bounded denoiser dubbed as VDTF.•Spatial–temporal information is utilized to generate adaptive thresholds.•The proposed PnP-VDTF algorithm can achieve high-quality reconstructions.•We explicitly prove that VDTF is a bounded denoiser.•We prove that PnP-VDTF can generate fixed-point convergent trajectories.