Shadow Detection and Removal from Solo Natural Image Based on Retinex Theory

Shadows are physical phenomena observed in most natural scenes. They can cause many problems in computer vision performance. The paper addresses the problem of shadow detection and removal from solo image of natural scenes. Our method is based on Retinex theory which is an image enhancement and illumination compensation model of the lightness and color perception of human vision. The approach proposed here does not use any special prior knowledge and assumptions. The shadow extraction algorithm originates from a simple idea that the human-vision-based Retinex has the natural ability to enhance the shadow region of an image no matter it is penumbrae or umbrae. The penumbrae and umbrae regions will be highlighted if we compare the Retinex-enhanced images with original images. Then through adding smooth light forcibly to shadow edges and introducing shadow edge masks, we reduce the effects of shadow edges in the Retinex enhancement processing. Experiment results validate the approach.