Bibliographic Details
| Title: |
Weighted least square filter via deep unsupervised learning. |
| Authors: |
Yang, Yang, Wu, Dan, Zeng, Lanling, Li, Zhuoran |
| Source: |
Multimedia Tools & Applications; Mar2024, Vol. 83 Issue 11, p31361-31377, 17p |
| Abstract: |
The weighted least square (WLS) filter is a popular edge-preserving image smoother that is particularly useful for detail enhancing and HDR tone mapping. However, it suffers from limited edge-preserving capability and high computational cost. Existing deep learning-based filters under the WLS framework are mostly based on supervised learning. They improve the efficiency but not the quality. In this paper, we propose a novel edge-preserving filter under the weighted least square framework based on deep unsupervised learning. According to the spatial-varying smoothing property of the edge-preserving filter, we propose a lightweight fully convolution neural network based on dilated convolutions with varying expanding factors. The proposed filter fully makes use of the 2D neighborhood information, thus it is able to suppress various artifacts. Thanks to the highly optimized framework for deep learning, the proposed filter is highly efficient, enabling the processing of 720P images at interactive rates (≈ 12 fps) on a modern desktop. Experimental results indicate that the proposed filter achieves the state-of-the-art smoothing quality. Therefore, our filter benefits a variety of tasks in the field of image processing and computer graphics. [ABSTRACT FROM AUTHOR] |
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| Database: |
Complementary Index |