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Local Texture Estimator for Implicit Representation Function
- Publication Year :
- 2021
-
Abstract
- Recent works with an implicit neural function shed light on representing images in arbitrary resolution. However, a standalone multi-layer perceptron 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 achieves favorable performance against existing deep SR methods within an arbitrary-scale factor. Furthermore, we demonstrate that our implementation takes the shortest running time compared to previous works.<br />Comment: CVPR 2022 camera-ready version (https://ipl.dgist.ac.kr/LTE_cvpr.pdf)
Details
- Database :
- arXiv
- Publication Type :
- Report
- Accession number :
- edsarx.2111.08918
- Document Type :
- Working Paper