Dual-Camera Super-Resolution with Aligned Attention Modules
ICCV 2021 (Oral Presentation)



We present a novel approach to reference-based super-resolution (RefSR) with the focus on dual-camera super-resolution (DCSR), which utilizes reference images for high-quality and high-fidelity results. Our proposed method generalizes the standard patch-based feature matching with spatial alignment operations. We further explore the dual-camera super-resolution that is one promising application of RefSR, and build a dataset that consists of 146 image pairs from the main and telephoto cameras in a smartphone. To bridge the domain gaps between real-world images and the training images, we propose a self-supervised domain adaptation strategy for real-world images. Extensive experiments on our dataset and a public benchmark demonstrate clear improvement achieved by our method over state of the art in both quantitative evaluation and visual comparisons.


Overview of our pipeline.



Zoom in for details.

4X SR results on CUFED5 testset can be found in this link. results


author = {Wang, Tengfei and Xie, Jiaxin and Sun, Wenxiu and Yan, Qiong and Chen, Qifeng},
title = {Dual-Camera Super-Resolution with Aligned Attention Modules},
booktitle = {International Conference on Computer Vision (ICCV)},
year = {2021}