Revisit Retinex Theory: Towards a Lightness-Aware Restorer for Underexposed Images
Revisit Retinex Theory: Towards a Lightness-Aware Restorer for Underexposed Images
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DOI：
10.1155/2020/1325705
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发表时间：
2020-07
影响因子：
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通讯作者：
Lin Zhang;Anqi Zhu;Ying Shen;Shengjie Zhao;Huijuan Zhang
中科院分区：
文献类型：
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作者：
Lin Zhang;Anqi Zhu;Ying Shen;Shengjie Zhao;Huijuan Zhang
我们研究了如何纠正曝光不足的图像。以往方法的瓶颈主要在于在处理不同曝光级别的图像时的自然性和稳健性。当面对曝光良好或曝光严重不足的图像时，它们可能会产生过度或不足的输出。在本文中，我们提出了一种新的基于retinex的方法，即LIAR(亮度感知恢复器的缩写)。亮度感知一词是指，估计的照度不仅是要调整的分量，而且还作为反映场景亮度的度量，确定调整的程度。这样，曝光不足的图像可以根据自身的亮度进行自适应恢复。在给定图像的情况下，说谎者首先使用专门设计的损失函数来估计其光照图，该损失函数可以确保结果的颜色一致性和纹理丰富性。然后进行自适应校正，以获得适当曝光的输出。Liar基于单幅测试图像的内部优化，不需要任何事先训练，这意味着它可以适应每幅图像的不同设置。此外，由于其简单性和稳定性，说谎者可以很容易地扩展到视频案例。实验证明，面对不同曝光级别的图像/视频，Liar可以实现高对比度和自然度的健壮和实时校正。相关代码和收集的数据可在https://cslinzhang.github.io/LiAR-Homepage/上公开获得。
We investigate how to correct exposure of underexposed images. The bottleneck of previous methods mainly lies in their naturalness and robustness when dealing with images with various exposure levels. When facing well-exposed or extremely underexposed images, they may produce over- or underenhanced outputs. In this paper, we propose a novel retinex-based approach, namely, LiAR (short for lightness-aware restorer). The word “lightness-aware” refers to that the estimated illumination not only is a component to be adjusted but also serves as a measure that reflects the brightness of the scene, determining the degree of adjustment. In this way, underexposed images can be restored adaptively according to their own brightness. Given an image, LiAR first estimates its illumination map using a specially designed loss function which can ensure the result’s color consistency and texture richness. Then adaptive correction is performed to get properly exposed output. LiAR is based on internal optimization of the single test image and does not need any prior training, implying that it can adapt itself to different settings per image. Additionally, LiAR can be easily extended to the video case due to its simplicity and stability. Experiments demonstrate that facing images/videos with various exposure levels, LiAR can achieve robust and real-time correction with high contrast and naturalness. The relevant code and collected data are publicly available at https://cslinzhang.github.io/LiAR-Homepage/ .