Multi-Level Spatial-Temporal Feature Aggregation and Alignment-Based Selective Residual Dense Propagation Module for HDR Video Reconstruction

Abstract

To reconstruct high dynamic range (HDR) video from alternating exposed low dynamic range (LDR) frames, the key is to address the misalignment and imprecise fusion caused by information loss and noise in ill-exposed regions. Following a coarse-to-fine manner, a Multi-level Spatial-Temporal feature aggregation and alignment-based Selective Residual Dense Propagation Network (MSTSRDPNet) is proposed. The Multi-level Spatial-Temporal aggregation extracts spatial-temporal features and aggregates them to mitigate information loss for fusion. The alignment-based Selective Residual Dense Propagation module reconstructs the aligned feature by using channel attention to redistribute feature weights while leveraging residual dense connections for information propagation. Experiments show that the proposed MSTSRDPNet outperforms all conventional methods on the synthetic dataset with PSNR-T, HDR-VQM, and HDR-VDP-2 scores of 44.64 dB, 86.83, and 73.9.