Lecca Michela, Messelodi Stefano
J Opt Soc Am A Opt Image Sci Vis. 2019 Aug 1;36(8):1423-1432. doi: 10.1364/JOSAA.36.001423.
A spatial color algorithm grounded on the Retinex theory is known as a Milano Retinex. This type of algorithm performs image enhancement by processing spatial and color cues in the neighborhood of each image pixel. Because this local, pixel-wise analysis is time consuming, optimization techniques are needed to expand the use of Milano Retinexes to applications that require fast or even real-time image processing. In this work, we propose SuPeR, an efficient optimization of the Milano Retinex local spatial color processing that exploits superpixels, which are as the regular, rectangular blocks of a grid that partitions the image support. Image enhancement is obtained by reworking channel-wise the intensity of each pixel based on the maximum color intensities of the blocks and on its distance from the blocks. The experiments, carried out on real-world image datasets, show good performance compared to other Milano Retinexes.
一种基于视网膜皮层理论的空间颜色算法被称为米兰视网膜皮层算法。这种算法通过处理每个图像像素邻域内的空间和颜色线索来执行图像增强。由于这种局部的、逐像素分析耗时较长，因此需要优化技术，以便将米兰视网膜皮层算法应用于需要快速甚至实时图像处理的应用中。在这项工作中，我们提出了SuPeR，这是对米兰视网膜皮层局部空间颜色处理的一种有效优化，它利用了超像素，超像素就如同划分图像区域的规则矩形网格块。通过基于块的最大颜色强度及其与块的距离，逐通道重新处理每个像素的强度来实现图像增强。在真实世界图像数据集上进行的实验表明，与其他米兰视网膜皮层算法相比，该算法具有良好的性能。
