Back to Search
Start Over
Global context aware dual channel pyramid model for robust image shadow removal.
- Source :
-
Engineering Applications of Artificial Intelligence . Jul2024:Part E, Vol. 133, pN.PAG-N.PAG. 1p. - Publication Year :
- 2024
-
Abstract
- Deep learning has made significant advancements in shadow removal. However, current methods primarily focus on local operations, leading to artifacts around shadow boundaries and inconsistent lighting between shadow and non-shadow regions. To address this issue, a global context aware dual channel pyramid model is proposed for robust image shadow removal in this paper. The model leverages a multi-scale channel attention framework based on transformers to capture global information. It consists of a shadow detection module and a dual-channel shadow interaction module to utilize non-shadow regions in aiding shadow restoration. Additionally, Winograd-based separable convolution attention shadow interaction attention is proposed to effectively expand the perceptual field and facilitate comprehensive utilization of contextual relevance between shadow and non-shadow regions. Extensive experiments are conducted on several popular public datasets such as ISTD, SRD and ISTD + to evaluate the effectiveness of our model. [ABSTRACT FROM AUTHOR]
- Subjects :
- *PYRAMIDS
*DEEP learning
*MULTISCALE modeling
Subjects
Details
- Language :
- English
- ISSN :
- 09521976
- Volume :
- 133
- Database :
- Academic Search Index
- Journal :
- Engineering Applications of Artificial Intelligence
- Publication Type :
- Academic Journal
- Accession number :
- 177749231
- Full Text :
- https://doi.org/10.1016/j.engappai.2024.108552