The-Learning-And-Vision-Atelier-LAVA / Stereo-Image-SR

With recent advances in stereo vision, dual cameras are commonly adopted in mobile phones and autonomous vehicles. Using the complementary information provided by binocular systems, the resolution of image pairs can be enhanced by stereo image super-resolution (SR) algorithms. In this repository, we first present a collection of datasets and papers on stereo image SR, together with their codes or repos. Then, we develop a benchmark to comprehensively evaluate milestone and state-of-the-art methods. Welcome to raise issues regarding our survey and submit novel results (better together with original files and source codes) to our benchmark.

Note: This repository will be updated on a regular basis, so stay tuned~~🎉🎉🎉

News:

• 2022-04-20: We add a new work Trans-SVSR that is accepted to CVPRW 2022.
• 2022-04-20: We add a new work NAFSSR to our repo, which is the Champion of the NTIRE 2022 Stereo Image Super-Resolution Challenge.

Challenges:

NTIRE 2022 Stereo Image Super-Resolution Challenge

Datasets:

Methods:

Benchmark

We benchmark several methods on the KITTI 2012, KITTI 2015, Middlebury and Flickr1024 datasets. The test sets used below can be downloaded from Google Drive and Baidu Drive (Key: NUDT). Note that, All these methods have been retrained on the same training set (60 images from the Middlebury dataset and 800 images from the Flickr1024 dataset) for fair comparison.

PSNR and SSIM metrics are used for quantitative evaluation, which are first calculated on each view (without boundary cropping) independently, then averaged between left and right views to generate the score of a scene. Finally, the score of a dataset is obtained by averaging the scores of all its scenes.

PSNR and SSIM values achieved by different methods for 2xSR:

PSNR and SSIM values achieved by different methods for 4xSR:

Recources

We provide some original super-resolved images and useful resources to facilitate researchers to reproduce the above results.

• The 2x/4x models of EDSR/RDN/RCAN retrained on stereo image datasets. Google Drive, Baidu Drive (Key: NUDT).
• The 2x/4x results of StereoSR. Google Drive, Baidu Drive (Key: NUDT).
• The 4x results of SRRes+SAM. Google Drive, Baidu Drive (Key: NUDT).
• The 2x/4x results of iPASSR. Google Drive and Baidu Drive (Key: NUDT).
• The 2x/4x results of BSSRnet. Google Drive and Baidu Drive (Key: NUDT).