EIN: Exposure-Induced Network for Single-Image HDR Reconstruction

Yue Liu, Zhangkai Ni, Peilin Chen, Shiqi Wang, Xinfeng Zhang, Hanli Wang, Sam Kwong

Abstract: Reconstructing high dynamic range (HDR) images from standard dynamic range (SDR) ones has received growing attention in recent years. A predominant problem of this task lies in the absence of texture and structural information in under/over-exposed regions. In this article, we propose an efficient and stable single-image HDR reconstruction method, namely exposure-induced network (EIN). More specifically, a dynamic range expansion branch (DB) is designed to expand the global dynamic range of the input SDR image. Moreover, two exposure-gated detail recovering branches for local over- (OB) and under- (UB) exposed regions are proposed to interact with the DB to progressively infer the texture and structural details with the learned confidence maps to resolve challenging ambiguities in such regions. The features from these three interactional branches are adaptively fused in the joint global–local decoder to reconstruct the final HDR image. The proposed network is trained based upon a large-scale dataset constructed with diverse content. Extensive experimental results demonstrate that the proposed model achieves consistent visual quality improvement for input SDR images with different exposures compared with state-of-the-art methods. The source code is available at: https://github.com/Yliu724/EIN.

External IDs:dblp:journals/tomccap/LiuNCWZWK25