1. Joint distortion rectification and super-resolution for self-driving scene perception.
- Author
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Zhao, Keyao, Liao, Kang, Lin, Chunyu, Liu, Meiqin, and Zhao, Yao
- Subjects
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PROBLEM solving , *GEOMETRIC distribution , *VISUAL fields , *COMPUTER vision , *EDUCATIONAL tests & measurements - Abstract
Fisheye lenses are widely applied in the self-driving system due to its large field of view (FOV), which also induces the strong radial distortion in captured data. Distortion rectification is a crucial preprocessing step for scene perception since the non-linear projection heavily destroys the original geometric distribution of objects. Previous methods rectify the distorted image with a plausible global distribution, but they perform poorly on the local appearance such as the detailed texture of vehicles and pedestrians, which are hard to meet the fine requirements of object detection and semantic recognition. To solve this problem, we propose a joint distortion rectification (DR) and super-resolution (SR) end-to-end framework to generate the realistic rectification result with a visually pleasing local appearance. To the best of our knowledge, this is the first parametric framework that combines the challenging techniques DR and SR in the field of computer vision, which learns the accurate mapping function from the distorted and low-resolution domain into the corrected and high-resolution domain simultaneously. Moreover, the devised network architecture using the channel attention mechanism and object-aware loss function enables better-reconstructed scenes compared with the state-of-the-art methods. Experimental results show that our approach achieves superior rectification and enhancement performance in both quantitative measure and visual qualitative appearance, contributing a comprehensive and promising vision algorithm to the perception of the self-driving scene. [ABSTRACT FROM AUTHOR]
- Published
- 2021
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