Searching a Compact Architecture for Robust Multi-Exposure Image Fusion

TL;DR

This paper introduces a novel architecture search-based method for robust multi-exposure image fusion that addresses misalignment and efficiency issues, achieving superior performance and faster inference.

Contribution

It proposes a self-alignment and detail repletion modules within a neural architecture search framework for improved multi-exposure image fusion.

Findings

Achieves 3.19% PSNR improvement in general scenarios.

Attains 23.5% better performance in misaligned scenarios.

Reduces inference time by 69.1%.

Abstract

In recent years, learning-based methods have achieved significant advancements in multi-exposure image fusion. However, two major stumbling blocks hinder the development, including pixel misalignment and inefficient inference. Reliance on aligned image pairs in existing methods causes susceptibility to artifacts due to device motion. Additionally, existing techniques often rely on handcrafted architectures with huge network engineering, resulting in redundant parameters, adversely impacting inference efficiency and flexibility. To mitigate these limitations, this study introduces an architecture search-based paradigm incorporating self-alignment and detail repletion modules for robust multi-exposure image fusion. Specifically, targeting the extreme discrepancy of exposure, we propose the self-alignment module, leveraging scene relighting to constrain the illumination degree for following alignment and feature extraction. Detail repletion is proposed to enhance the texture details of scenes. Additionally, incorporating a hardware-sensitive constraint, we present the fusion-oriented architecture search to explore compact and efficient networks for fusion. The proposed method outperforms various competitive schemes, achieving a noteworthy 3.19\% improvement in PSNR for general scenarios and an impressive 23.5\% enhancement in misaligned scenarios. Moreover, it significantly reduces inference time by 69.1\%. The code will be available at https://github.com/LiuZhu-CV/CRMEF.