Abstract: In order to achieve a single image’s fast shadow removal, this paper proposes an image shadow detection and removal method based on LAB color space. Firstly, we convert the RGB image into the LAB image, and then detect the shadow image by the edge detection. Then, the image matching the average chromaticity value of the shadow area and the shadowless area is obtained, through analyzing, calculating and re-integrating different color channels. Finally, single image shadow is removed by color correction and edge correction. In order to verify the feasibility and effectiveness of the proposed method, the performance indexes, that is, peak signal to noise ratio (PSNR) and structural similarity (SSIM) are used to evaluate the image shadow removal results objectively. And we compare the proposed method with two typical image shading methods. The results show that the performance index of this method is the highest. In particular, the PSNR performance indexes of three groups of experiments are 17.4721, 17.6206, 17.3048, while the SSIM performance indexes are 0.8192, 0.8344, 0.8027. And the image feature information is clear after shadow removing. Overall, good shadowless effect has been achieved that the retained structure information is closer to the real shadowless scene image.

Key words: image shadow removal, LAB color space, shadow detection, reintegration, peak signal to noise ratio(PSNR), structural similarity index measure (SSIM)