Retinex theory is developed mainly to decompose an image into the illumination and reflectance components by analyzing local image derivatives. In this theory, larger derivatives are attributed to the changes in reflectance, while smaller derivatives are emerged in the smooth illumination. In this paper, we utilize exponentiated local derivatives (with an exponent $\gamma $ ) of an observed image to generate its structure map and texture map. The structure map is produced by been amplified with $\gamma >1$ , while the texture map is generated by been shrank with $\gamma < 1$ . To this end, we design exponential filters for the local derivatives, and present their capability on extracting accurate structure and texture maps, influenced by the choices of exponents $\gamma $ . The extracted structure and texture maps are employed to regularize the illumination and reflectance components in Retinex decomposition. A novel Structure and Texture Aware Retinex (STAR) model is further proposed for illumination and reflectance decomposition of a single image. We solve the STAR model by an alternating optimization algorithm. Each sub-problem is transformed into a vectorized least squares regression, with closed-form solutions. Comprehensive experiments on commonly tested datasets demonstrate that, the proposed STAR model produce better quantitative and qualitative performance than previous competing methods, on illumination and reflectance decomposition, low-light image enhancement, and color correction. The code is publicly available at https://github.com/csjunxu/STAR .

中文翻译：

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STAR：结构和纹理感知Retinex模型

Retinex理论的发展主要是通过分析局部图像导数，将图像分解为照明和反射率分量。在此理论中，较大的导数归因于反射率的变化，而较小的导数则出现在平滑照明中。在本文中，我们使用指数局部导数（具有指数 $ \伽马$ ）观察图像以生成其结构图和纹理图。结构图是通过用 $ \伽玛> 1 $ ，而纹理图是通过缩小生成的 $ \伽玛<1 $ 。为此，我们为局部导数设计了指数过滤器，并介绍了它们在提取受指数选择影响的准确结构和纹理贴图方面的能力 $ \伽马$ 。提取的结构图和纹理图用于规范Retinex分解中的照明和反射率分量。进一步提出了一种新颖的结构和纹理感知Retinex（STAR）模型，用于分解单个图像的照明和反射率。我们通过交替优化算法求解STAR模型。每个子问题都转化为具有封闭形式解的矢量化最小二乘回归。在常用测试数据集上进行的综合实验表明，所提出的STAR模型在照明和反射率分解，低光图像增强和色彩校正方面比以前的竞争方法具有更好的定量和定性性能。该代码可在以下位置公开获得https://github.com/csjunxu/STAR 。