SSAB-DGUNet: enhancing shadow removal in images using a multistage progressive approach with spatial attention

Yinyin Xu; Weijian Hu; Lingfang Li; Baoshan Li

doi:10.1117/12.3021343

25 March 2024 SSAB-DGUNet: enhancing shadow removal in images using a multistage progressive approach with spatial attention

Yinyin Xu, Weijian Hu, Lingfang Li, Baoshan Li

Author Affiliations +

Proceedings Volume 13089, Fifteenth International Conference on Graphics and Image Processing (ICGIP 2023); 130890W (2024) https://doi.org/10.1117/12.3021343
Event: Fifteenth International Conference on Graphics and Image Processing (ICGIP 2023), 2023, Suzhou, China

Abstract

Image shadow removal is an essential image preprocessing task. In practical production environments, effective image shadow removal methods can significantly enhance the performance of subsequent image-based tasks. However, current methods for image shadow removal still encounter issues such as artifacts, color deviations, and blurriness due to factors including the capturing environment and algorithmic efficiency. This paper proposes an image shadow removal method using spatial attention, integrating physical and deep learning models. By incorporating multi-scale feature learning and preserving spatial details, the approach integrates shadow spatial attention modules, perceptual loss, and edge loss to improve the shadow removal effect. Experimental results demonstrate that the proposed method achieves a PSNR value of 36.14dB and an SSIM exceeding 98% in the ISTD dataset, with the RMSE reduced to 6.54. These outcomes affirm the efficiency and superiority of the proposed method in addressing the challenges of image shadow removal tasks.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Yinyin Xu, Weijian Hu, Lingfang Li, and Baoshan Li "SSAB-DGUNet: enhancing shadow removal in images using a multistage progressive approach with spatial attention", Proc. SPIE 13089, Fifteenth International Conference on Graphics and Image Processing (ICGIP 2023), 130890W (25 March 2024); https://doi.org/10.1117/12.3021343

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available