Intrinsic Image Sequence Decomposition Using Low-Rank Sparse Model

Abstract

Intrinsic image decomposition from a single image or image sequences is always a challenging task in computer vision and image processing due to the ill-posed characteristics. In order to attain a reasonable estimation of intrinsic images, in this paper, we present a low-rank sparse model (LRSM) to derive intrinsic images from an image sequence of the same scene under various lightings. Due to the dependance of varying lightings and the excellent edge-preserving ability of the total variation constraint, both are joined together to formulate the LRSM as a spatiotemporal prior. When considering the relationships among images of a scene as well as those between color channels of a color image, the proposed model involves a complex objective function and makes the solving much more difficult than that on a single gray-scale image. Therefore, we design an iterative numerical scheme based on the alternating direction method of multipliers framework to solve the objective function efficiently. We further specify the application of the proposed method to object recoloring. The experimental results demonstrate that the proposed LSRM and the iterative scheme are effective and efficient.

Keywords

• Intrinsic image decomposition
• image sequence
• low-rank sparse constraints
• ADMM