Work

Reconstructing High Dynamic Range Image from a Single Low Dynamic Range Image Using Histogram Learning

Year: 2024

Type: article

Abstract: High dynamic range imaging is an important field in computer vision. Compared with general low dynamic range (LDR) images, high dynamic range (HDR) images represent a larger luminance range, making the images closer to the real scene. In this paper, we propose an approach for HDR image reconstruction from a single LDR image based on histogram learning. First, the dynamic range of an LDR image is expanded to an extended dynamic range (EDR) image. Then, histogram learning is established to predict the intensity distribution of an HDR image of the EDR image. Next, we use histogram matching to reallocate pixel intensities. The final HDR image is generated through regional adjustment using reinforcement learning. By decomposing low-frequency and high-frequency information, the proposed network can predict the lost high-frequency details while expanding the intensity ranges. We conduct the experiments based on HDR-Real and HDR-EYE datasets. The quantitative and qualitative evaluations have demonstrated the effectiveness of the proposed approach compared to the previous methods.

Source: Applied Sciences

DOI: 10.3390/app14219847

Authors: Huei-Yung Lin, Yi-Rung Lin, Wen-Chieh Lin, Chin-Chen Chang

Institutions: National Taipei University of Technology, National Chung Cheng University, National Yang Ming Chiao Tung University, National United University

Language: English

Topic: Image Enhancement Techniques

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $2,728

Funder: National Science and Technology Council