Perceptual Multi-Exposure Fusion

• URL: http://arxiv.org/abs/2210.09604v2
• Date: Wed, 19 Oct 2022 06:58:48 GMT
• Title: Perceptual Multi-Exposure Fusion
• Authors: Xiaoning Liu
• Abstract summary: This paper presents a perceptual multi-exposure fusion method that ensures fine shadow/highlight details but with lower complexity than detailenhanced methods. We build a large-scale multiexposure benchmark dataset suitable for static scenes, which contains 167 image sequences. Experiments on the constructed dataset demonstrate that the proposed method exceeds existing eight state-of-the-art approaches in terms of visually and MEF-SSIM value.
• Score: 0.5076419064097732
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: As an ever-increasing demand for high dynamic range (HDR) scene shooting, multi-exposure image fusion (MEF) technology has abounded. In recent years, multi-scale exposure fusion approaches based on detail-enhancement have led the way for improvement in highlight and shadow details. Most of such methods, however, are too computationally expensive to be deployed on mobile devices. This paper presents a perceptual multi-exposure fusion method that not just ensures fine shadow/highlight details but with lower complexity than detailenhanced methods. We analyze the potential defects of three classical exposure measures in lieu of using detail-enhancement component and improve two of them, namely adaptive Wellexposedness (AWE) and the gradient of color images (3-D gradient). AWE designed in YCbCr color space considers the difference between varying exposure images. 3-D gradient is employed to extract fine details. We build a large-scale multiexposure benchmark dataset suitable for static scenes, which contains 167 image sequences all told. Experiments on the constructed dataset demonstrate that the proposed method exceeds existing eight state-of-the-art approaches in terms of visually and MEF-SSIM value. Moreover, our approach can achieve a better improvement for current image enhancement techniques, ensuring fine detail in bright light.

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