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 recti-fication 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 de-pended 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. In addition, we build a new large-scale fisheye image dataset to evaluate our method, which contains 50,0 0 0 training images and 50 0 0 test images. Experimental results show the proposed method can better handle images with a wide range of distortion. (c) 2021 Published by Elsevier B.V.
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