Retinex theory-based shadow detection and removal in single outdoor image

Purpose – Shadows, the common phenomena in most outdoor scenes, bring many problems in practical image processing. Shadow detection and removal, especial in uncalibrated outdoor image, is still a difficult problem. The purpose of this paper is to detect and to remove shadows in single outdoor image based on retinex theory. Design/methodology/approach – The shadow extraction algorithm originates from a simple idea that the human-vision-based retinex has the natural ability to enhance the shadow regions of an image no matter it is penumbrae or umbrae. Shadows are detected by comparing the retinexenhanced images with original images in the paper. The shadow removal algorithm in the paper deals with the shadow regions and non-shadow regions in the images separately using the retinex enhancement algorithm. Through adding smooth light forcibly to shadow edges and introducing shadow edge masks, the authors reduce the effects of shadow edges in shadow removal processing. Findings – Some real single outdoor images with the umbra regions and those with penumbra regions are both experimented in the paper. Experimental results validate the feasibility of the approach. Originality/value – The approach proposed here does not use any special prior knowledge and assumptions. The feasibility of this method is testified for detecting and removing both penumbrae and umbrae in the outdoor images.