Rowlands D Andrew, Finlayson Graham D
Colour & Imaging Lab, School of Computing Sciences, University of East Anglia, Norwich NR4 7TJ, UK.
J Imaging. 2024 Aug 22;10(8):204. doi: 10.3390/jimaging10080204.
In the convolutional retinex approach to image lightness processing, an image is filtered by a centre/surround operator that is designed to mitigate the effects of shading (illumination gradients), which in turn compresses the dynamic range. Typically, the parameters that define the shape and extent of the filter are tuned to provide visually pleasing results, and a mapping function such as a logarithm is included for further image enhancement. In contrast, a statistical approach to convolutional retinex has recently been introduced, which is based upon known or estimated autocorrelation statistics of the image albedo and shading components. By introducing models for the autocorrelation matrices and solving a linear regression, the optimal filter is obtained in closed form. Unlike existing methods, the aim is simply to objectively mitigate shading, and so image enhancement components such as a logarithmic mapping function are not included. Here, the full mathematical details of the method are provided, along with implementation details. Significantly, it is shown that the shapes of the autocorrelation matrices directly impact the shape of the optimal filter. To investigate the performance of the method, we address the problem of shading removal from text documents. Further experiments on a challenging image dataset validate the method.
在用于图像亮度处理的卷积视网膜皮层算法中，图像通过一个中心/环绕算子进行滤波，该算子旨在减轻阴影（光照梯度）的影响，进而压缩动态范围。通常，定义滤波器形状和范围的参数会进行调整以提供视觉上令人满意的结果，并且会包含一个诸如对数的映射函数以进行进一步的图像增强。相比之下，最近引入了一种基于卷积视网膜皮层算法的统计方法，该方法基于图像反照率和阴影成分的已知或估计自相关统计信息。通过引入自相关矩阵的模型并求解线性回归，以封闭形式获得最优滤波器。与现有方法不同，其目的仅仅是客观地减轻阴影，因此不包括诸如对数映射函数之类的图像增强成分。在此，提供了该方法的完整数学细节以及实现细节。值得注意的是，结果表明自相关矩阵的形状直接影响最优滤波器的形状。为了研究该方法的性能，我们解决了从文档中去除阴影的问题。在具有挑战性的图像数据集上进行的进一步实验验证了该方法。
