IEEE Trans Image Process. 2018 Dec;27(12):5802-5812. doi: 10.1109/TIP.2018.2858541. Epub 2018 Jul 23.
Milano Retinex is a family of spatial color algorithms inspired by Retinex and mainly devoted to the image enhancement. In the so-called point-based sampling Milano Retinex algorithms, this task is accomplished by processing the color of each image pixel based on a set of colors sampled in its surround. This paper presents STAR, a segmentation based approximation of the point-based sampling Milano Retinex approaches: it replaces the pixel-wise image sampling by a novel, computationally efficient procedure that detects once for all the color and spatial information relevant to image enhancement from clusters of pixels output by a segmentation. The experiments reported here show that STAR performs similarly to previous point-based sampling Milano Retinex approaches and that STAR enhancement improves the accuracy of the well-known algorithm scale-invariant feature transform on the description and matching of photographs captured under difficult light conditions.
米兰视网膜是一种受视网膜启发的空间色彩算法家族，主要致力于图像增强。在所谓的基于点的采样米兰视网膜算法中，这项任务是通过基于图像像素周围采集的一组颜色处理每个图像像素的颜色来完成的。本文提出了 STAR，这是一种基于分割的基于点的采样米兰视网膜算法的近似方法：它通过一种新颖的、计算效率高的过程来替代像素级的图像采样，该过程一次性检测到由分割输出的像素簇中与图像增强相关的颜色和空间信息。本文报道的实验表明，STAR 的性能与以前的基于点的采样米兰视网膜算法相似，并且 STAR 增强可以提高著名算法尺度不变特征变换在描述和匹配困难光照条件下拍摄的照片的准确性。
