Deriving Intrinsic Images from Image Sequences

Intrinsic images are a useful midlevel description of scenes proposed by Barrow and Tenenbaum [1]. An image is decomposed into two images: a reflectance image and an illumination image. Finding such a decomposition remains a difficult problem in computer vision. Here we focus on a slightly easier problem: given a sequence of images where the reflectance is constant and the illumination changes, can we recover illumination images and a single reflectance image? We show that this problem is still illposed and suggest approaching it as a maximum-likelihood estimation problem. Following recent work on the statistics of natural images, we use a prior that assumes that illumination images will give rise to sparse filter outputs. We show that this leads to a simple, novel algorithm for recovering reflectance images. We illustrate the algorithm's performance on real and synthetic image sequences. In: Proc ICCV (2001)

• Type: Conference
• Year: 2001
• Where: ICCV
• Authors: Yair Weiss
• Keywords: Computer Vision, ICCV 2001, Illumination Images, Maximum-likelihood Estimation Problem, Reflectance Images, Single Reflectance Image, Synthetic Image Sequences