At this, the diffuse component is further decomposed into albedo and shadingĬomponents. For this purpose, we present a novel specularity extraction algorithm based on a multi-scale intensity decomposition and chroma inpainting. Unlike state-of-the-art methods that assume only diffuse reflectance, we consider both diffuse and specular pixels. In this work, we present a system for intrinsic decomposition of RGB-D images on smartphones and the algorithmic as well as design choices therein.
When provided with correlating image and depth data, intrinsic scene decomposition can be facilitated using depth-based priors, which nowadays is easy to acquire with high-end smartphones by utilizing their depth sensors. The inverse problem setting, where multiple unknowns are solved given a single known pixel-value, is highly under-constrained. Intrinsic decomposition refers to the problem of estimating scene characteristics, such as albedo and shading, when one view or multiple views of a scene are provided. The tests effected on real and artificial multispectral dermatological images have shown the good performance of our method compared to two of the most used methods. In order to quantify numerically the performance of our method, we also propose a new protocol to artificially mix a specular reflection image with a diffuse reflection image.
We exploit this first version of the sources in a second step to initialize our Non-negative Matrix Factorization algorithm instead of the random initialization that is used by most of the existing methods and that considerably affects their performances. The key idea of our method is based on a first step which consists to estimate the number of sources involved, by applying a Principal Component Analysis, then a first version of these sources by applying an Independent Component Analysis to our images. In this paper we propose a new method for removing specular reflection from multispectral dermatological images which is based on blind source separation using Non-negative Matrix Factorization.
0 kommentar(er)
