# Learning Parallax Attention for Stereo Image Super-Resolution

arXiv: https://arxiv.org/abs/1903.05784 · HTML: https://mlsanity.com/paper/1903.05784/

## Key facts

- Cited by 56 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Mar 2019.

## Built on by (most cited first, 30 of 56)

- [SwinIR: Image Restoration Using Swin Transformer](https://mlsanity.com/paper/2108.10257/) (2021)
- [VRT: A Video Restoration Transformer](https://mlsanity.com/paper/2201.12288/) (2022)
- [NAFSSR: Stereo Image Super-Resolution Using NAFNet](https://mlsanity.com/paper/2204.08714/) (2022)
- [Hierarchical Conditional Flow: A Unified Framework for Image Super-Resolution and Image Rescaling](https://mlsanity.com/paper/2108.05301/) (2021)
- [CiaoSR: Continuous Implicit Attention-in-Attention Network for Arbitrary-Scale Image Super-Resolution](https://mlsanity.com/paper/2212.04362/) (2022)
- [SwinFIR: Revisiting the SwinIR with Fast Fourier Convolution and Improved Training for Image Super-Resolution](https://mlsanity.com/paper/2208.11247/) (2022)
- [Mutual Affine Network for Spatially Variant Kernel Estimation in Blind Image Super-Resolution](https://mlsanity.com/paper/2108.05302/) (2021)
- [Bilateral Grid Learning for Stereo Matching Networks](https://mlsanity.com/paper/2101.01601/) (2021)
- [A Comprehensive Review of Deep Learning-based Single Image Super-resolution](https://mlsanity.com/paper/2102.09351/) (2021)
- [Feedback Network for Mutually Boosted Stereo Image Super-Resolution and Disparity Estimation](https://mlsanity.com/paper/2106.00985/) (2021)
- [Learning Non-Local Spatial-Angular Correlation for Light Field Image Super-Resolution](https://mlsanity.com/paper/2302.08058/) (2023)
- [A Systematic Survey of Deep Learning-based Single-Image Super-Resolution](https://mlsanity.com/paper/2109.14335/) (2021)
- [Learning to Immunize Images for Tamper Localization and Self-Recovery](https://mlsanity.com/paper/2210.15902/) (2022)
- [NTIRE 2022 Challenge on Stereo Image Super-Resolution: Methods and Results](https://mlsanity.com/paper/2204.09197/) (2022)
- [SwinFSR: Stereo Image Super-Resolution using SwinIR and Frequency Domain Knowledge](https://mlsanity.com/paper/2304.12556/) (2023)
- [Hybrid Transformer and CNN Attention Network for Stereo Image Super-resolution](https://mlsanity.com/paper/2305.05177/) (2023)
- [A Dive into SAM Prior in Image Restoration](https://mlsanity.com/paper/2305.13620/) (2023)
- [Data Acquisition and Preparation for Dual-reference Deep Learning of Image Super-Resolution](https://mlsanity.com/paper/2108.02348/) (2021)
- [Dense Dual-Attention Network for Light Field Image Super-Resolution](https://mlsanity.com/paper/2110.12114/) (2021)
- [TWR-MCAE: A Data Augmentation Method for Through-the-Wall Radar Human Motion Recognition](https://mlsanity.com/paper/2301.02488/) (2023)
- [NTIRE 2023 Challenge on Light Field Image Super-Resolution: Dataset, Methods and Results](https://mlsanity.com/paper/2304.10415/) (2023)
- [PFT-SSR: Parallax Fusion Transformer for Stereo Image Super-Resolution](https://mlsanity.com/paper/2303.13807/) (2023)
- [Stereo Waterdrop Removal with Row-wise Dilated Attention](https://mlsanity.com/paper/2108.03457/) (2021)
- [AFFIRM: Affinity Fusion-based Framework for Iteratively Random Motion correction of multi-slice fetal brain MRI](https://mlsanity.com/paper/2205.05851/) (2022)
- [Learning Parallax Transformer Network for Stereo Image JPEG Artifacts Removal](https://mlsanity.com/paper/2207.07335/) (2022)
- [Monocular Road Planar Parallax Estimation](https://mlsanity.com/paper/2111.11089/) (2021)
- [Depth Estimation by Combining Binocular Stereo and Monocular Structured-Light](https://mlsanity.com/paper/2203.10493/) (2022)
- [Perception-Oriented Stereo Image Super-Resolution](https://mlsanity.com/paper/2207.06617/) (2022)
- [Decoupled Cross-Scale Cross-View Interaction for Stereo Image Enhancement in The Dark](https://mlsanity.com/paper/2211.00859/) (2022)
- [Cross-View Hierarchy Network for Stereo Image Super-Resolution](https://mlsanity.com/paper/2304.06236/) (2023)

## Abstract (arXiv)

Stereo image pairs can be used to improve the performance of super-resolution (SR) since additional information is provided from a second viewpoint. However, it is challenging to incorporate this information for SR since disparities between stereo images vary significantly. In this paper, we propose a parallax-attention stereo superresolution network (PASSRnet) to integrate the information from a stereo image pair for SR. Specifically, we introduce a parallax-attention mechanism with a global receptive field along the epipolar line to handle different stereo images with large disparity variations. We also propose a new and the largest dataset for stereo image SR (namely, Flickr1024). Extensive experiments demonstrate that the parallax-attention mechanism can capture correspondence between stereo images to improve SR performance with a small computational and memory cost. Comparative results show that our PASSRnet achieves the state-of-the-art performance on the Middlebury, KITTI 2012 and KITTI 2015 datasets.