Single Image HDR Synthesis with Histogram Learning

High dynamic range imaging aims for a more accurate representation of the scene. It provides a large luminance coverage to yield the human perception range. In this paper, we present a technique to synthesize an HDR image from the LDR input. The proposed two-stage approach expands the dynamic range and predict its histogram with cumulative histogram learning. Histogram matching is then carried out to reallocate the pixel intensity. In the second stage, HDR images are constructed using reinforcement learning with pixel-wise rewards for local consistency adjustment. Experiments are conducted on HDR-Real and HDR-EYE datasets. The quantitative evaluation on HDR-VDP-2, PSNR, and SSIM have demonstrated the effectiveness compared to the state-of-the-art techniques.