Local Texture Estimator for Implicit Representation Function

• URL: http://arxiv.org/abs/2111.08918v1
• Date: Wed, 17 Nov 2021 06:01:17 GMT
• Title: Local Texture Estimator for Implicit Representation Function
• Authors: Jaewon Lee and Kyong Hwan Jin
• Abstract summary: Local Texture Estimator (LTE) is a dominant-frequency estimator for natural images. 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.
• Score: 10.165529175855712
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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