Lin Yi-Hsien, Lu Yi-Chang
IEEE Trans Image Process. 2022;31:4897-4908. doi: 10.1109/TIP.2022.3189805. Epub 2022 Jul 22.
Low-light photography conditions degrade image quality. This study proposes a novel Retinex-based low-light enhancement method to correctly decompose an input image into reflectance and illumination. Subsequently, we can improve the viewing experience by adjusting the illumination using intensity and contrast enhancement. Because image decomposition is a highly ill-posed problem, constraints must be properly imposed on the optimization framework. To meet the criteria of ideal Retinex decomposition, we design a nonconvex L norm and apply shrinkage mapping to the illumination layer. In addition, edge-preserving filters are introduced using the plug-and-play technique to improve illumination. Pixel-wise weights based on variance and image gradients are adopted to suppress noise and preserve details in the reflectance layer. We choose the alternating direction method of multipliers (ADMM) to solve the problem efficiently. Experimental results on several challenging low-light datasets show that our proposed method can more effectively enhance image brightness as compared with state-of-the-art methods. In addition to subjective observations, the proposed method also achieved competitive performance in objective image quality assessments.
低光照摄影条件会降低图像质量。本研究提出了一种基于Retinex的新型低光照增强方法，以将输入图像正确分解为反射率和光照。随后，我们可以通过使用强度和对比度增强来调整光照，从而改善观看体验。由于图像分解是一个高度不适定问题，因此必须在优化框架上适当施加约束。为了满足理想Retinex分解的标准，我们设计了一个非凸L范数，并将收缩映射应用于光照层。此外，使用即插即用技术引入边缘保留滤波器以改善光照。采用基于方差和图像梯度的逐像素权重来抑制噪声并保留反射率层中的细节。我们选择交替方向乘子法（ADMM）来有效地解决该问题。在几个具有挑战性的低光照数据集上的实验结果表明，与现有方法相比，我们提出的方法可以更有效地增强图像亮度。除了主观观察外，该方法在客观图像质量评估中也取得了有竞争力的性能。
