[论文解读] A Bio-Inspired Multi-Exposure Fusion Framework for Low-light Image Enhancement

Zhenqiang Ying, Ge Li+1 more|arXiv (Cornell University)|Nov 2, 2017

Image Enhancement Techniques参考文献 36被引用 248

一句话总结

本论文提出一种受生物启发的多曝光融合框架用于低光增强，提出一种双曝光融合算法，通过估计照明、利用相机响应模型合成一个曝光充足的图像，并使用学习得到的权重图将其与输入图像融合，以减少对比度和亮度失真。

ABSTRACT

Low-light images are not conducive to human observation and computer vision algorithms due to their low visibility. Although many image enhancement techniques have been proposed to solve this problem, existing methods inevitably introduce contrast under- and over-enhancement. Inspired by human visual system, we design a multi-exposure fusion framework for low-light image enhancement. Based on the framework, we propose a dual-exposure fusion algorithm to provide an accurate contrast and lightness enhancement. Specifically, we first design the weight matrix for image fusion using illumination estimation techniques. Then we introduce our camera response model to synthesize multi-exposure images. Next, we find the best exposure ratio so that the synthetic image is well-exposed in the regions where the original image is under-exposed. Finally, the enhanced result is obtained by fusing the input image and the synthetic image according to the weight matrix. Experiments show that our method can obtain results with less contrast and lightness distortion compared to that of several state-of-the-art methods.

研究动机与目标

• Motivate improved low-light image enhancement that preserves natural lightness and contrast.
• Propose a framework inspired by human visual system to generate and fuse multi-exposure images from a single input.
• Develop a dual-exposure fusion algorithm with illumination-based weighting and a camera response model.
• Demonstrate reduced contrast and lightness distortion compared with state-of-the-art methods across multiple datasets.

提出的方法

• Introduce a four-part framework: Multi-Exposure Sampler, Generator, Evaluator, and Combiner.
• Estimate a scene illumination map T by solving a regularized optimization to refine a local-consistent illumination.
• Model a Brightness Transform Function (BTF) g using a two-parameter form g(P,k)=β P^γ and derive a camera response function (CRF) f from a comparametric equation, yielding g(P,k)=e^{b(1-k^{a})} P^{k^{a}} with parameters derived from camera characteristics.
• Compute an optimal exposure ratio k by maximizing the entropy of the brightness channel after synthesis, and fuse the input image with the synthetic well-exposed image using a weight map Ŵ.
• Provide a closed-form illumination map solution via a linear system for efficiency, and validate through extensive experiments on public datasets.

实验结果

研究问题

• RQ1 Can a bio-inspired multi-exposure fusion framework improve low-light image enhancement by balancing exposure across regions?
• RQ2 How can illumination estimation and a camera response model be combined to synthesize a well-exposed image from a single low-light input?
• RQ3 Does dual-exposure fusion with an illumination-guided weight map reduce contrast and lightness distortion compared to existing methods?

主要发现

• The proposed Ours method yields lower lightness distortion (LOE) across multiple datasets compared to MSRCR, Dong, NPE, LIME, MF, and SRIE.
• In terms of Visual Information Fidelity (VIF), the proposed method achieves higher scores on several datasets, indicating better preservation of visual information.
• The method demonstrates favorable contrast preservation with lower distortion as measured by DRIM across datasets.
• The approach balances enhancement and naturalness, reducing halo artifacts and noise compared with several baselines.
• The authors report competitive time costs and provide open-source code for reproducibility.