A high-low frequency guided generative adversarial network for shadow removal

Xiya Li; Huan Yang; Chenglin Sun; Hua Li

doi:10.1117/12.3048153

3 October 2024 A high-low frequency guided generative adversarial network for shadow removal

Xiya Li, Huan Yang, Chenglin Sun, Hua Li

Proceedings Volume 13272, Fifth International Conference on Computer Vision and Data Mining (ICCVDM 2024); 1327222 (2024) https://doi.org/10.1117/12.3048153
Event: 5th International Conference on Computer Vision and Data Mining (ICCVDM 2024), 2024, Changchun, China

Abstract

The occurrence of shadows in images leads to a damage in image quality and also hinders the performance of downstream visual tasks. Despite significant progress in existing shadow removal methods, they are limited in synthesizing highfrequency signal details, resulting in insufficient synthesis quality. Among them, most of methods based on Generative Adversarial Networks (GANs), while capable of removing shadows, often introduce image artifacts and blurring, particularly underperforming in the processing of high-frequency signals, which impacts the clarity of the image after shadow removal. To address these issues, we propose a high-low frequency guided GAN method for image shadow removal. Concretely, our method decomposes extracted features into multiple frequency components (i.e., low and high frequency) and retains the image's contours and structural information through low-frequency attention skip connections. Meanwhile, high-frequency attention skip connections are employed to alleviate the difficulty for the generator to synthesize details, providing the generator with rich frequency information. Additionally, we also introduce a wavelet-based frequency loss function to reduce the discrepancies between the generated image and the real image in the frequency domain, effectively mitigating the occurrence of artifacts and blurring. Finally, extensive experiments conducted on the ISTD, AISTD, and SRD datasets demonstrate the significant effectiveness of our proposed method, which not only enhances the efficiency of shadow removal but also markedly improves the high-frequency detail quality of the de-shadowed images.

Citation Download Citation

Xiya Li, Huan Yang, Chenglin Sun, and Hua Li "A high-low frequency guided generative adversarial network for shadow removal", Proc. SPIE 13272, Fifth International Conference on Computer Vision and Data Mining (ICCVDM 2024), 1327222 (3 October 2024); https://doi.org/10.1117/12.3048153

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

Members: $17.00

Non-members: $21.00 ADD TO CART

PROCEEDINGS
7 PAGES

DOWNLOAD PAPER SAVE TO MY LIBRARY

GET CITATION

RIGHTS & PERMISSIONS

Get copyright permission Get copyright permission on Copyright Marketplace

KEYWORDS

Shadows

Gallium nitride

Image quality

Image enhancement

Image processing

Feature extraction

Performance modeling

Show All Keywords

Keywords/Phrases

Search In:

Publication Years