MobileMEF: Fast and Efficient Method for Multi-Exposure Fusion

• URL: http://arxiv.org/abs/2408.07932v2
• Date: Tue, 1 Oct 2024 14:26:16 GMT
• Title: MobileMEF: Fast and Efficient Method for Multi-Exposure Fusion
• Authors: Lucas Nedel Kirsten, Zhicheng Fu, Nikhil Ambha Madhusudhana,
• Abstract summary: We propose a new method for multi-exposure fusion based on an encoder-decoder deep learning architecture. Our model is capable of processing 4K resolution images in less than 2 seconds on mid-range smartphones.
• Score: 0.6261722394141346
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Recent advances in camera design and imaging technology have enabled the capture of high-quality images using smartphones. However, due to the limited dynamic range of digital cameras, the quality of photographs captured in environments with highly imbalanced lighting often results in poor-quality images. To address this issue, most devices capture multi-exposure frames and then use some multi-exposure fusion method to merge those frames into a final fused image. Nevertheless, most traditional and current deep learning approaches are unsuitable for real-time applications on mobile devices due to their heavy computational and memory requirements. We propose a new method for multi-exposure fusion based on an encoder-decoder deep learning architecture with efficient building blocks tailored for mobile devices. This efficient design makes our model capable of processing 4K resolution images in less than 2 seconds on mid-range smartphones. Our method outperforms state-of-the-art techniques regarding full-reference quality measures and computational efficiency (runtime and memory usage), making it ideal for real-time applications on hardware-constrained devices. Our code is available at: https://github.com/LucasKirsten/MobileMEF.

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