[CVPR 2023]Learning Steerable Function for Efficient Image Resampling
Channel: Jiacheng Li
Date: 2023-06-01

Quick Intro:

Image resampling is a technique that manipulates the image according to a geometric transformation, such as downsampling and sheering.

Typically, resampling is achieved through interpolation.

In this work, we propose a novel method of Learning Resampling Function to make the interpolation process adaptive to local structures.

Instead of adopting fixed function, our method assigns spatially-varying steerable resampling functions to input image pixels and learns to predict the hyper-parameters that determine the orientations of these resampling functions with a neural network.

Accelerated by Look Up Table, our method runs as fast as interpolation, generalizes well to arbitrary transformations, and outperforms interpolation significantly.

Authors:
Jiacheng Li*, Chang Chen*, Wei Huang, Zhiqiang Lang, Fenglong Song, Youliang Yan, and Zhiwei Xiong

Project page: https://lerf.pages.dev/
Paper: https://openaccess.thecvf.com/content/CVPR2023/html/Li_Learning_Steerable_Function_for_Efficient_Image_Resampling_CVPR_2023_paper.html