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Local Texture Estimator for Implicit Representation Function

2021-11-17 06:01:17
Jaewon Lee, Kyong Hwan Jin

Abstract

Recent works with an implicit neural function shed light on representing images in arbitrary resolution. However, a standalone multi-layer perceptron (MLP) shows limited performance in learning high-frequency components. In this paper, we propose a Local Texture Estimator (LTE), a dominant-frequency estimator for natural images, enabling an implicit function to capture fine details while reconstructing images in a continuous manner. When jointly trained with a deep super-resolution (SR) architecture, LTE is capable of characterizing image textures in 2D Fourier space. We show that an LTE-based neural function outperforms existing deep SR methods within an arbitrary-scale for all datasets and all scale factors. Furthermore, we demonstrate that our implementation takes the shortest running time compared to previous works. Source code will be open.

Abstract (translated)

URL

https://arxiv.org/abs/2111.08918

PDF

https://arxiv.org/pdf/2111.08918.pdf


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