HDR-LMDA: A Local Area-Based Mixed Data Augmentation Method for Hdr Video Reconstruction

Mainstream image manipulation-based data augmentation methods (e.g., CutMix) undermine the integrity of extracted features, which leads to limited effect for pixel-level image processing tasks. In this paper, a local area-based mixed data augmentation method called HDR-LMDA for HDR video reconstruction is proposed. Within it, the local exposure augmentation (LEA) applies different exposures to original LDR inputs among different regions, while the local RGB permutation (LRP) shuffles the color channels of the random patch instead of the entire frame. By mixing both two operations, the model is forced to learn how to apply concise ill-exposure recovery and color processing within the same training process. Experiments demonstrate that HDR-LMDA achieves a better PSNR-T boost of 0.93dB, compared with conventional works under the same conditions.