# Dual-Camera Super-Resolution with Aligned Attention Modules

arXiv: https://arxiv.org/abs/2109.01349 · HTML: https://mlsanity.com/paper/2109.01349/

## Key facts

- Cited by 34 papers in the map (data through 14 Oct 2026).
- Builds on 28 of its 47 references that are in the map.
- First posted on arXiv 3 Sep 2021.

## Builds on (most cited first, 28 of 28)

- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Perceptual Losses for Real-Time Style Transfer and Super-Resolution](https://mlsanity.com/paper/1603.08155/) (2016)
- [Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network](https://mlsanity.com/paper/1609.04802/) (2016)
- [Enhanced Deep Residual Networks for Single Image Super-Resolution](https://mlsanity.com/paper/1707.02921/) (2017)
- [Deformable Convolutional Networks](https://mlsanity.com/paper/1703.06211/) (2017)
- [Spatial Transformer Networks](https://mlsanity.com/paper/1506.02025/) (2015)
- [Image Super-Resolution Using Very Deep Residual Channel Attention Networks](https://mlsanity.com/paper/1807.02758/) (2018)
- [ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks](https://mlsanity.com/paper/1809.00219/) (2018)
- [Image Super-Resolution Using Deep Convolutional Networks](https://mlsanity.com/paper/1501.00092/) (2014)
- [Deep Image Prior](https://mlsanity.com/paper/1711.10925/) (2017)
- [Residual Dense Network for Image Super-Resolution](https://mlsanity.com/paper/1802.08797/) (2018)
- [Deep Laplacian Pyramid Networks for Fast and Accurate Super-Resolution](https://mlsanity.com/paper/1704.03915/) (2017)
- [Toward Real-World Single Image Super-Resolution: A New Benchmark and A New Model](https://mlsanity.com/paper/1904.00523/) (2019)
- [Deep Learning for Image Super-resolution: A Survey](https://mlsanity.com/paper/1902.06068/) (2019)
- [Learning Texture Transformer Network for Image Super-Resolution](https://mlsanity.com/paper/2006.04139/) (2020)
- ["Zero-Shot" Super-Resolution using Deep Internal Learning](https://mlsanity.com/paper/1712.06087/) (2017)
- [Blind Super-Resolution With Iterative Kernel Correction](https://mlsanity.com/paper/1904.03377/) (2019)
- [EnhanceNet: Single Image Super-Resolution Through Automated Texture Synthesis](https://mlsanity.com/paper/1612.07919/) (2016)
- [RankSRGAN: Generative Adversarial Networks with Ranker for Image Super-Resolution](https://mlsanity.com/paper/1908.06382/) (2019)
- [The Contextual Loss for Image Transformation with Non-Aligned Data](https://mlsanity.com/paper/1803.02077/) (2018)
- [Contextual Residual Aggregation for Ultra High-Resolution Image Inpainting](https://mlsanity.com/paper/2005.09704/) (2020)
- [Image Super-Resolution by Neural Texture Transfer](https://mlsanity.com/paper/1903.00834/) (2019)
- [Zoom To Learn, Learn To Zoom](https://mlsanity.com/paper/1905.05169/) (2019)
- [Fast Patch-based Style Transfer of Arbitrary Style](https://mlsanity.com/paper/1612.04337/) (2016)
- [CrossNet: An End-to-end Reference-based Super Resolution Network using Cross-scale Warping](https://mlsanity.com/paper/1807.10547/) (2018)
- [Understanding Deformable Alignment in Video Super-Resolution](https://mlsanity.com/paper/2009.07265/) (2020)
- [Image Inpainting with External-internal Learning and Monochromic Bottleneck](https://mlsanity.com/paper/2104.09068/) (2021)
- [Maintaining Natural Image Statistics with the Contextual Loss](https://mlsanity.com/paper/1803.04626/) (2018)

## Built on by (most cited first, 30 of 34)

- [High-Fidelity GAN Inversion for Image Attribute Editing](https://mlsanity.com/paper/2109.06590/) (2021)
- [DeSRA: Detect and Delete the Artifacts of GAN-based Real-World Super-Resolution Models](https://mlsanity.com/paper/2307.02457/) (2023)
- [Bootstrap Motion Forecasting With Self-Consistent Constraints](https://mlsanity.com/paper/2204.05859/) (2022)
- [Self-Supervised Learning for Real-World Super-Resolution from Dual Zoomed Observations](https://mlsanity.com/paper/2203.01325/) (2022)
- [Reference-based Video Super-Resolution Using Multi-Camera Video Triplets](https://mlsanity.com/paper/2203.14537/) (2022)
- [Generating Aligned Pseudo-Supervision from Non-Aligned Data for Image Restoration in Under-Display Camera](https://mlsanity.com/paper/2304.06019/) (2023)
- [RRSR:Reciprocal Reference-based Image Super-Resolution with Progressive Feature Alignment and Selection](https://mlsanity.com/paper/2211.04203/) (2022)
- [LMR: A Large-Scale Multi-Reference Dataset for Reference-based Super-Resolution](https://mlsanity.com/paper/2303.04970/) (2023)
- [DC2: Dual-Camera Defocus Control by Learning to Refocus](https://mlsanity.com/paper/2304.03285/) (2023)
- [Reference-based Image and Video Super-Resolution via C2-Matching](https://mlsanity.com/paper/2212.09581/) (2022)
- [C2PD: Continuity-Constrained Pixelwise Deformation for Guided Depth Super-Resolution](https://mlsanity.com/paper/2501.07688/) (2025)
- [Imaging for All-Day Wearable Smart Glasses](https://mlsanity.com/paper/2504.13060/) (2025)
- [BadRefSR: Backdoor Attacks Against Reference-based Image Super Resolution](https://mlsanity.com/paper/2502.20943/) (2025)
- [Degradation-agnostic Correspondence from Resolution-asymmetric Stereo](https://mlsanity.com/paper/2204.01429/) (2022)
- [Point-and-Shoot All-in-Focus Photo Synthesis from Smartphone Camera Pair](https://mlsanity.com/paper/2304.04917/) (2023)
- [KeDuSR: Real-World Dual-Lens Super-Resolution via Kernel-Free Matching](https://mlsanity.com/paper/2312.17050/) (2023)
- [Toward Real-World Light Field Super-Resolution](https://mlsanity.com/paper/2305.18994/) (2023)
- [RefVSR++: Exploiting Reference Inputs for Reference-based Video Super-resolution](https://mlsanity.com/paper/2307.02897/) (2023)
- [Efficient Hybrid Zoom using Camera Fusion on Mobile Phones](https://mlsanity.com/paper/2401.01461/) (2024)
- [Self-Supervised Learning for Real-World Super-Resolution from Dual and Multiple Zoomed Observations](https://mlsanity.com/paper/2405.02171/) (2024)
- [NeuriCam: Key-Frame Video Super-Resolution and Colorization for IoT Cameras](https://mlsanity.com/paper/2207.12496/) (2022)
- [Reference-based Motion Blur Removal: Learning to Utilize Sharpness in the Reference Image](https://mlsanity.com/paper/2307.02875/) (2023)
- [UltraZoom: Generating Gigapixel Images from Regular Photos](https://mlsanity.com/paper/2506.13756/) (2025)
- [DeeDSR: Towards Real-World Image Super-Resolution via Degradation-Aware Stable Diffusion](https://mlsanity.com/paper/2404.00661/) (2024)
- [Consistent Diffusion: Denoising Diffusion Model with Data-Consistent Training for Image Restoration](https://mlsanity.com/paper/2412.12550/) (2024)
- [HSTR-Net: Reference Based Video Super-resolution with Dual Cameras](https://mlsanity.com/paper/2310.12092/) (2023)
- [The Devil is in the Details: Boosting Guided Depth Super-Resolution via Rethinking Cross-Modal Alignment and Aggregation](https://mlsanity.com/paper/2401.08123/) (2024)
- [ReWiTe: Realistic Wide-angle and Telephoto Dual Camera Fusion Dataset via Beam Splitter Camera Rig](https://mlsanity.com/paper/2404.10584/) (2024)
- [DM3Net: Dual-Camera Super-Resolution via Domain Modulation and Multi-scale Matching](https://mlsanity.com/paper/2506.06993/) (2025)
- [Modulate and Reconstruct: Learning Hyperspectral Imaging from Misaligned Smartphone Views](https://mlsanity.com/paper/2507.01835/) (2025)

## Abstract (arXiv)

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.