Single Exposure High Dynamic Range Image Reconstruction Based on Deep Dual-Branch Network

• Main Authors: Nianjin Ye, Yongqing Huo, Shuaicheng Liu, Hanlin Li
• Format: Article
• Language: English
• Published: IEEE 2021-01-01
• Series: IEEE Access
• Subjects: High dynamic range imaging; deep learning; single exposure image; dual branch network
• Online Access: https://ieeexplore.ieee.org/document/9316280/

In this article, we relate the operation of single-frame-based high dynamic range (HDR) image reconstruction to the following two tasks: 1) highlight suppression in over-exposed areas and 2) noise elimination in under-exposed areas. The common goal of both tasks is to preserve or even enhance the details and improve the visibility of scenes when generating the HDR image. These two tasks can be solved separately with fundamentally different ways. In this article, we propose a dual-branch network to process the over- and under- exposed areas respectively for single-frame-based HDR image reconstruction. First, the low dynamic range (LDR) image is normalized, linearized and inputted into both branches, and the masks of the over- and under- exposed regions are calculated to detect the improper exposed areas. Second, the over- and under- exposed areas are restored and enhanced by the two branches respectively, at the same time, the color distribution is learned to obtain more consistent color saturation between the generated HDR image and the ground truth. Third, the output of the two branches and the linearized input LDR image are combined based on the masks to obtain the reconstructed HDR image. Extensive experiments show that the proposed method can efficiently restore the texture and color of the over-exposed areas, suppress the noise of the under-exposed areas, and obtain the HDR image with good contrast, clear details and high structural fidelity of the ground truth image appearance.