MERF: A Practical HDR-Like Image Generator via Mutual-Guided Learning Between Multi-Exposure Registration and Fusion
Wenhui Hong, Hao Zhang, Jiayi Ma
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.