DYNAMIC RANGE TRANSFORMER (DRT): LEARNING ENHANCED LOG-PERCEPTUAL INFORMATION WITH SWIN-FOURIER CONVOLUTION NETWORK FOR HDR IMAGING

The image obtained using an image sensor with limited dynamic range cannot perfectly represent the various lighting conditions of the real world. Various HDR methods have been studied for expanding the dynamic range in a single image. However, it is difficult to avoid ghosting artifacts caused by the movement of the subject over time and the corresponding texture loss. To solve these problems, we present a novel HDR image acquisition method via dynamic range transformer (DrT) that learns enhanced log-perceptual information using Swin-Fourier convolutional neural network as a backbone. When training the DrT with Swin-Fourier network, it estimates the attention map to obtain an HDR image by minimizing the enhanced log-perceptual (ELP) loss. The Swin-Fourier network considers both local and global contexts simultaneously, which reduces ghosting and texture loss. By learning ELP, it also minimizes color distortion and restores fine details of the dynamic range. Experimental results demonstrate that the HDR results obtained using DrT show reduced color distortion, significantly decreased ghosting artifacts, and texture loss compared to conventional methods. We provide implementation code of our proposed methods in https://github.com/HeunSeungLim/DrT