MERF: A Practical HDR-Like Image Generator via Mutual-Guided Learning Between Multi-Exposure Registration and Fusion.

ABSTRACT

In this paper, we present a novel high dynamic range (HDR)-like image generator that utilizes mutual-guided learning between multi-exposure registration and fusion, leading to promising dynamic multi-exposure image fusion. The method consists of three main components the registration network, the fusion network, and the dual attention network which seamlessly integrates registration and fusion processes. Initially, within the registration network, the estimation of deformation fields among multi-exposure image sequences is conducted following an exposure-invariant feature extraction phase. This leads to enhanced accuracy by mitigating discrepancies across domains. Subsequently, the fusion network utilizes a progressive frequency fusion module in two distinct stages, addressing color correction and detail preservation within low and high-frequency domains, respectively. To facilitate the mutual enhancement of the registration and fusion networks, we undertake a mutual-guided learning strategy encompassing their physical connection and constraint paradigm. Firstly, a dual attention network bridges the registration and fusion networks, addressing ghosting, which is beyond the scope of registration and facilitates information exchange between input images. Secondly, a meticulously designed generative adversarial network-like iterative training schema guides the overall network framework, thereby yielding high-quality HDR-like images through mutual enhancement. Comprehensive experiments on publicly available datasets validate the superiority of our method over existing state-of-the-art approaches.