Abstract
Recently, various deep learning-based high dynamic range (HDR) imaging methods have been proposed to overcome the performance limitation of simply merging or fusion of multiple low dynamic range (LDR) images. However, early deep learning-based methods did not fully exploit the inter-image relationship between different exposures. T o consider inter-image features without the dataset dependency, this paper presents a novel HDR method using three-dimensional residual dense network (3D RDN). We first reconstruct a set of differently exposed LDR images in video format. Next, we take the reconstructed video as an input to the 3D RDN, which generates an HDR image as output. Experimental results show that the proposed method provides higher score compared with state-of-the-arts HDR methods. To the best of authors knowledge, the proposed work first adopted the 3D RDN in the field of HDR imaging using more than three LDR images without any additional pre-or post-processing.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
