High Dynamic Range Imaging Based on Bidirectional Structural Similarities and Weighted Low-Rank Matrix Completion

Abstract

High dynamic range (HDR) imaging, aiming to increase the dynamic range of an image by merging multiexposure images, has attracted much attention. Ghosts are often observed in a resultant image, due to camera motion and object motion in the scene. Low-rank matrix completion (LRMC) provides an effective tool to remove ghosts. However, user specification of the included or excluded regions is required. In this paper, we propose a novel HDR imaging method based on bidirectional structural similarities and weighted low-rank matrix completion. In our method, we first propose the bidirectional structural similarities containing forward-projection structural similarity (FPSS) and backward-projection structural similarity (BPSS) to divide each image into four groups: motion region, saturated region in the source image, saturated region in the reference image, and static and unsaturated regions. Then, the weight maps and the motion maps constructed based on FPSS and BPSS are introduced in the weighted LRMC model to reconstruct the background irradiance maps. Experiments are conducted on several challenging image sets with complex scene, and the results show that the proposed method outperforms three current state-of-the-art methods and Photoshop cs6 and is robust to the reference image.