Affiliation:
1. Advanced Visual Intelligence Lab (AVILAB), Yeungnam University, Gyeongsan-si 38541, Republic of Korea
Abstract
Several studies in computer vision have examined specular removal, which is crucial for object detection and recognition. This research has traditionally been divided into two tasks: specular highlight removal, which focuses on removing specular highlights on object surfaces, and reflection removal, which deals with specular reflections occurring on glass surfaces. In reality, however, both types of specular effects often coexist, making it a fundamental challenge that has not been adequately addressed. Recognizing the necessity of integrating specular components handled in both tasks, we constructed a specular-light (S-Light) DB for training single-image-based deep learning models. Moreover, considering the absence of benchmark datasets for quantitative evaluation, the multi-scale normalized cross correlation (MS-NCC) metric, which considers the correlation between specular and diffuse components, was introduced to assess the learning outcomes.
Funder
National Research Foundation of Korea
Reference55 articles.
1. A survey on deep multimodal learning for computer vision: Advances, trends, applications, and datasets;Bayoudh;Vis. Comput.,2021
2. Summaira, J., Li, X., Shoib, A.M., Li, S., and Abdul, J. (2021). Recent advances and trends in multimodal deep learning: A review. arXiv.
3. Guo, X., Cao, X., and Ma, Y. (2014, January 23–28). Robust separation of reflection from multiple images. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Columbus, OH, USA.
4. Su, T., Zhou, Y., Yu, Y., and Du, S. (2022). Highlight Removal of Multi-View Facial Images. Sensors, 22.
5. A computational approach for obstruction-free photography;Xue;ACM Trans. Graph.,2015