In this paper, a novel method to decompose an image into “intrinsic images” is introduced. This decomposition is based on the dichromatic model which separates the influence of specular and diffuse reflections. The separation of these components is very important in several applications of image analysis including segmentation, classification, recoloring, and specularity removal. The proposed method is based on two simple deconvolution steps. The method aims to be generic: it is applicable to any kind of image (i.e. RGB as well as multispectral) and does not rely on a learning step. The method is applied to three datasets including multispectral and RGB images. The algorithm is compared to recent algorithms from the literature and gives similar or better results than the state of the art.

本文介绍了一种将图像分解为“固有图像”的新颖方法。该分解基于双色模型，该模型将镜面反射和漫反射的影响分开。这些成分的分离在图像分析的几种应用中非常重要，包括分割，分类，重新着色和镜面反射去除。所提出的方法基于两个简单的反卷积步骤。该方法旨在通用：它适用于任何类型的图像（即RGB以及多光谱），并且不依赖于学习步骤。该方法应用于包括多光谱和RGB图像的三个数据集。该算法与文献中的最新算法进行了比较，并且比现有技术给出了相似或更好的结果。