Although a large number of methods have been proposed for low-light image enhancement, there still remain challenges for these methods to simultaneously achieve excellent sharpness/resolution, high calculation efficiency as well as visual pleasure requirements. In this communication, we propose a new low-light image enhancement method based on the degradation model to overcome this dilemma. Specifically, we regard the low-light image enhancement as a special inverse problem of image degradation, and then the task of low-light enhancement is logically embedded in the iterative back-projection (IBP) framework. Meanwhile, an adaptive gamma correction is utilized to adaptively adjust the brightness, and then the IBP framework is transferred to the logarithmic domain instead of the spatial domain for further acceleration. Besides, a simple and effective pre-processing strategy is proposed to pre-enhance the low-light input while making the enhanced image clarify (or visual pleasure). Extensive experimental results on public databases and seven state-of-the-art benchmarks consistently demonstrate the effectiveness and efficiency of the proposed method both visually and quantitatively.

尽管已经提出了大量用于弱光图像增强的方法，但这些方法在同时实现出色的清晰度/分辨率、高计算效率以及视觉愉悦要求方面仍然存在挑战。在本次交流中，我们提出了一种基于退化模型的新的低光图像增强方法来克服这一困境。具体来说，我们将弱光图像增强视为图像退化的特殊逆问题，然后将弱光增强的任务逻辑嵌入到迭代背投影（IBP）框架中。同时，利用自适应伽马校正来自适应调整亮度，然后将 IBP 框架转移到对数域而不是空间域进行进一步加速。除了，提出了一种简单有效的预处理策略来预增强低光输入，同时使增强后的图像变得清晰（或视觉愉悦）。公共数据库和七个最先进的基准测试的广泛实验结果一致地从视觉和定量两个方面证明了所提出方法的有效性和效率。