Model-Free Rectification via Cascaded Distortion Model and Enhanced Backward Flow Network
Jie Zhao, Shikui Wei, Yakun Chang, Tao Ruan, Yao Zhao · IEEE Transactions on Circuits and Systems for Video Technology · 2024
Model-free rectification methods are limited by poor rectification quality and low generalization. This paper introduces a novel framework for enhancing model-free distortion rectification by addressing the limitations of existing methods. Our proposed method incorporates a Cascaded Distortion Model (CDM) inspired by fisheye lenses, which combines multiple reversible distortion models to create a versatile and comprehensive framework. By utilizing backward warping instead of forward warping, our approach overcomes the limitations of non-integer pixel positions and grid artifacts. Furthermore, our data synthesis method facilitates the fusion of different distortion models, bridging the distribution gap and improving generalization. To improve flow prediction accuracy, we introduce a two-stream network that incorporates both forward and backward flow branches. This approach enhances the prediction of backward flow and improves overall distortion rectification performance. We evaluate our method on large-scale synthetic datasets and real distorted images, and the results demonstrate its superior performance in both qualitative and quantitative experiments.