Selective TransHDR: Transformer-Based Selective HDR Imaging Using Ghost Region Mask

Abstract

The primary issue in high dynamic range (HDR) imaging is the removal of ghost artifacts afforded when merging multi-exposure low dynamic range images. In the weakly misaligned region, ghost artifacts can be suppressed using convolutional neural network (CNN)-based methods. However, in highly misaligned regions, it is necessary to extract features from the global region because the necessary information does not exist in the local region. Therefore, the CNN-based methods specialized for local features extraction cannot obtain satisfactory results. To address this issue, we propose a transformer-based selective HDR image reconstruction network that uses a ghost region mask. The proposed method separates a given image into ghost and non-ghost regions, and then, selectively applies either the CNN or the transformer. The proposed selective transformer module divides an entire image into several regions to effectively extract the features of each region for HDR image reconstruction, thereby extracting the whole information required for HDR reconstruction in the ghost regions from the entire image. Extensive experiments conducted on several benchmark datasets demonstrate the superiority of the proposed method over existing state-of-the-art methods in terms of the mitigation of ghost artifacts.

J. W. Song and Y.-I. Park—Equal contribution.