Hierarchical Neural Operator Transformer with Learnable Frequency-aware Loss Prior for Arbitrary-scale Super-resolution

20 May 2024

Papers citing "Hierarchical Neural Operator Transformer with Learnable Frequency-aware Loss Prior for Arbitrary-scale Super-resolution"

6 / 6 papers shown

Title
Explorable INR: An Implicit Neural Representation for Ensemble Simulation Enabling Efficient Spatial and Parameter Exploration Yi-Tang Chen Haoyu Li Neng Shi Xihaier Luo Wei-ping Xu Han-Wei Shen 34 0 0 01 Apr 2025
Super-Resolution Neural Operator Min Wei Xuesong Zhang SupR 33 40 0 05 Mar 2023
OPE-SR: Orthogonal Position Encoding for Designing a Parameter-free Upsampling Module in Arbitrary-scale Image Super-Resolution Gaochao Song Luo Zhang Ran Su Jianfeng Shi Yingchuan He Qian Sun SupR 37 12 0 02 Mar 2023
Deep Fourier Up-Sampling Man Zhou Huikang Yu Jie Huang Fengmei Zhao Jinwei Gu Chen Change Loy Deyu Meng Chongyi Li OOD 42 51 0 11 Oct 2022
Fourier Neural Operator for Parametric Partial Differential Equations Zong-Yi Li Nikola B. Kovachki Kamyar Azizzadenesheli Burigede Liu K. Bhattacharya Andrew M. Stuart Anima Anandkumar AI4CE 203 2,254 0 18 Oct 2020
Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network Wenzhe Shi Jose Caballero Ferenc Huszár J. Totz Andrew P. Aitken Rob Bishop Daniel Rueckert Zehan Wang SupR 183 5,138 0 16 Sep 2016