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Searching a Compact Architecture for Robust Multi-Exposure Image Fusion by Liu, Zhu; Liu, Jinyuan; Wu, Guanyao; Chen, Zihang; Fan, Xin; Liu, Risheng is a scholarly article available to read on EtoBox.

What is Searching a Compact Architecture for Robust Multi-Exposure Image Fusion about?

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 outperfo

Author

Liu, Zhu; Liu, Jinyuan; Wu, Guanyao; Chen, Zihang; Fan, Xin; Liu, Risheng

Published

2023

Language

EN