#5821. DQN-based gradual fisheye image rectification

June 2026publication date
Proposal available till 15-05-2025
4 total number of authors per manuscript0 $

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Journal’s subject area:
Signal Processing;
Software;
Computer Vision and Pattern Recognition;
Artificial Intelligence;
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Abstract:
Fisheye image rectification is a classic and important task in the computer vision area, which is generally treated as a pre-processing step in many application scenarios. Most of the existing fisheye image rectification methods focused mainly on building a direct (one-step) projection relationship between fisheye images and corrected images. Although these methods have achieved impressive performance, they depended heavily on data distribution and cannot work well on images whose distortion parameters are out of range. To address this issue, we propose a multi-step gradual image rectification scheme. In particular, we treat the fisheye image rectification problem as one Markov Decision Process and employ a widely-used deep reinforcement learning method (i.e., Deep Q-Network) to solve the problem.
Keywords:
Deep Q-Network; Fisheye image; Rectification

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