Shadow removal using sparse representation over local dictionaries

Abstract

The presence of shadow in an image is a major problem associated with various visual processing applications such as object recognition, traffic surveillance and segmentation. In this paper, we introduce a method to remove the shadow from a real image using the morphological diversities of shadows and sparse representation. The proposed approach first generates an invariant image and further processing is applied to the invariant image. Here, shadow removal is formulated as a decomposition problem that uses separate local dictionaries for shadow and nonshadow parts, without using single global or fixed generic dictionary. These local dictionaries are constructed from the patches extracted from the residual of the image obtained after invariant image formation. Finally, non-iterative Morphological Component Analysis-based image decomposition using local dictionaries is performed to add the geometric component to the non-shadow part of the image so as to obtain shadow free version of the input image. The proposed approach of shadow removal works well for indoor and outdoor images, and the performance has been compared with previous methods and found to be better in terms of RMSE.

Keywords

• Shadow
• Dictionary learning
• Shadow removal
• Sparse coding
• MCA