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Deep Bilateral Learning for Stereo Image Super-Resolution
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DOI：10.1109/LSP.2021.3066125.png)
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摘要
En 中文
Bilateral filter has demonstrated its effectiveness in many traditional methods for image restoration tasks. In this letter, we incorporate the idea of bilateral grid processing in a CNN framework and propose a bilateral stereo super-resolution network (BSSRnet). Specifically, we use a parallax-attention module to incorporate information from left and right views to learn content-aware bilateral filters. Then, these bilateral filters are used to recover missing details at different spatial locations while preserving stereo consistency. Our network is fully differentiable and is robust to both content and disparity variations. Comparative results show that our BSSRnet achieves state-of-the-art performance on the Flickr1024, Middlebury, KITTI 2012 and KITTI 2015 datasets. Source code is available at.
Keyword:
Feature extraction
Superresolution
Training
Convolution
Kernel
Task analysis
Spatial resolution
Bilateral filter
recursive
stereo image
super-resolution
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9.6
论文数:
1.1W
被引数:
1.7W
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