Intrinsic Image Decomposition: A Comprehensive Review

Abstract

Image understanding and analysis is one of the important tasks in the image processing. Multiple factors influence the appearance of an object in an image. However, extracting the intrinsic images from the observer image can eliminate the environmental impact effectively and make the image understanding more accurately. The intrinsic images represent the inherent shape, color and texture information of the object. Intrinsic image decomposition is recovering shading image and reflectance image from a single input image and remains a challenging problem because of its severely ill-posed problem. In order to deal with these problems, researches have proposed various algorithms for decomposing the intrinsic image. In this paper we survey the recent advances in intrinsic image decomposition. First, we introduce the existing datasets for intrinsic image decomposition. Second, we introduce and analyze the existing intrinsic image decomposition algorithms. Finally, we use the existing algorithms to experiment on the intrinsic image datasets, and analyze and summarize the experimental results.