Pixel-wise Orthogonal Decomposition for Color Illumination Invariant and Shadow-free Image
This work addresses the challenge of removing shadows in outdoor images for applications in computer vision, offering a novel approach that avoids shadow detection or statistical learning, though it is incremental in improving shadow-free image generation.
The paper tackles the problem of generating shadow-free images from single outdoor images by proposing a pixel-wise orthogonal decomposition method based on physically-based shadow invariants, resulting in an algorithm that preserves texture and color information effectively and is validated through experiments.
In this paper, we propose a novel, effective and fast method to obtain a color illumination invariant and shadow-free image from a single outdoor image. Different from state-of-the-art methods for shadow-free image that either need shadow detection or statistical learning, we set up a linear equation set for each pixel value vector based on physically-based shadow invariants, deduce a pixel-wise orthogonal decomposition for its solutions, and then get an illumination invariant vector for each pixel value vector on an image. The illumination invariant vector is the unique particular solution of the linear equation set, which is orthogonal to its free solutions. With this illumination invariant vector and Lab color space, we propose an algorithm to generate a shadow-free image which well preserves the texture and color information of the original image. A series of experiments on a diverse set of outdoor images and the comparisons with the state-of-the-art methods validate our method.