High Quality Reference Feature for Two Stage Bracketing Image Restoration and Enhancement
Xiaoxia Xing, Hyunhee Park, Fan Wang, Ying Zhang, Sejun Song, Changho Kim, Xiangyu Kong · 2024
In a low-light environment, it is difficult to obtain high-quality or high-resolution images with sharp details and high dynamic range (HDR) without noise or blur. To solve this problem, the Bracketing Image Restoration and Enhancement integrates Dnoise, Deblur, HDR Reconstruction, and Super Resolution techniques into a unified framework. However, we find that most methods select the image that aligns with GT as the reference image. Since the details of the reference image are not good enough, seriously affects the feature fusion, which finally leads to details being blurred. To generate a high dynamic range and a high-quality image, we propose a two-stage Bracketing method named RT-IRE. In the first stage, we generate the high-quality reference feature to guide feature fusion, remove the degradation, and reconstruct HDR to get coarse results. The second stage learns the residuals between the coarse result and the GT, which further enhances and generates details. Extensive experiments show the effectiveness of the proposed module. In particular, RT-IRE won two champions in the NTIRE 2024 Bracketing Image Restoration and Enhancement Challenge.