Perceptual Multi-Exposure Fusion

TL;DR

This paper introduces a perceptual multi-exposure fusion method that enhances highlight and shadow details with lower computational complexity, suitable for mobile devices, and outperforms existing methods on a large benchmark dataset.

Contribution

The paper proposes a novel multi-exposure fusion approach that improves classical exposure measures and reduces computational cost compared to detail-enhancement methods.

Findings

Outperforms eight state-of-the-art methods in visual quality and MEF-SSIM.

Ensures fine details in bright regions and shadow areas.

Validated on a large-scale static scene dataset with 167 sequences.

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.