Learning Local Implicit Fourier Representation for Image Warping

• URL: http://arxiv.org/abs/2207.01831v1
• Date: Tue, 5 Jul 2022 06:30:17 GMT
• Title: Learning Local Implicit Fourier Representation for Image Warping
• Authors: Jaewon Lee, Kwang Pyo Choi, Kyong Hwan Jin
• Abstract summary: We propose a local texture estimator for image warping (LTEW) followed by an implicit neural representation to deform images into continuous shapes. Our LTEW-based neural function outperforms existing warping methods for asymmetric-scale SR and homography transform.
• Score: 11.526109213908091
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Image warping aims to reshape images defined on rectangular grids into arbitrary shapes. Recently, implicit neural functions have shown remarkable performances in representing images in a continuous manner. However, a standalone multi-layer perceptron suffers from learning high-frequency Fourier coefficients. In this paper, we propose a local texture estimator for image warping (LTEW) followed by an implicit neural representation to deform images into continuous shapes. Local textures estimated from a deep super-resolution (SR) backbone are multiplied by locally-varying Jacobian matrices of a coordinate transformation to predict Fourier responses of a warped image. Our LTEW-based neural function outperforms existing warping methods for asymmetric-scale SR and homography transform. Furthermore, our algorithm well generalizes arbitrary coordinate transformations, such as homography transform with a large magnification factor and equirectangular projection (ERP) perspective transform, which are not provided in training.

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