# Dense Dual-Attention Network for Light Field Image Super-Resolution

arXiv: https://arxiv.org/abs/2110.12114 · HTML: https://mlsanity.com/paper/2110.12114/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 29 of its 73 references that are in the map.
- First posted on arXiv 23 Oct 2021.

## Builds on (most cited first, 29 of 29)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification](https://mlsanity.com/paper/1502.01852/) (2015)
- [CBAM: Convolutional Block Attention Module](https://mlsanity.com/paper/1807.06521/) (2018)
- [Enhanced Deep Residual Networks for Single Image Super-Resolution](https://mlsanity.com/paper/1707.02921/) (2017)
- [Image Super-Resolution Using Very Deep Residual Channel Attention Networks](https://mlsanity.com/paper/1807.02758/) (2018)
- [Image Super-Resolution Using Deep Convolutional Networks](https://mlsanity.com/paper/1501.00092/) (2014)
- [Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network](https://mlsanity.com/paper/1609.05158/) (2016)
- [Accurate Image Super-Resolution Using Very Deep Convolutional Networks](https://mlsanity.com/paper/1511.04587/) (2015)
- [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)
- [Deep Learning for Image Super-resolution: A Survey](https://mlsanity.com/paper/1902.06068/) (2019)
- [Deeply-Recursive Convolutional Network for Image Super-Resolution](https://mlsanity.com/paper/1511.04491/) (2015)
- [Deep Learning for Single Image Super-Resolution: A Brief Review](https://mlsanity.com/paper/1808.03344/) (2018)
- [A Deep Journey into Super-resolution: A survey](https://mlsanity.com/paper/1904.07523/) (2019)
- [Learning Parallax Attention for Stereo Image Super-Resolution](https://mlsanity.com/paper/1903.05784/) (2019)
- [EPINET: A Fully-Convolutional Neural Network Using Epipolar Geometry for Depth from Light Field Images](https://mlsanity.com/paper/1804.02379/) (2018)
- [Channel-wise and Spatial Feature Modulation Network for Single Image Super-Resolution](https://mlsanity.com/paper/1809.11130/) (2018)
- [Deep Video Super-Resolution using HR Optical Flow Estimation](https://mlsanity.com/paper/2001.02129/) (2020)
- [Parallax Attention for Unsupervised Stereo Correspondence Learning](https://mlsanity.com/paper/2009.08250/) (2020)
- [Spatial-Angular Interaction for Light Field Image Super-Resolution](https://mlsanity.com/paper/1912.07849/) (2019)
- [Light Field Spatial Super-resolution via Deep Combinatorial Geometry Embedding and Structural Consistency Regularization](https://mlsanity.com/paper/2004.02215/) (2020)
- [Symmetric Parallax Attention for Stereo Image Super-Resolution](https://mlsanity.com/paper/2011.03802/) (2020)
- [Light Field Image Super-Resolution Using Deformable Convolution](https://mlsanity.com/paper/2007.03535/) (2020)
- [A 4D Light-Field Dataset and CNN Architectures for Material Recognition](https://mlsanity.com/paper/1608.06985/) (2016)
- [MDCN: Multi-scale Dense Cross Network for Image Super-Resolution](https://mlsanity.com/paper/2008.13084/) (2020)
- [Selective Light Field Refocusing for Camera Arrays Using Bokeh Rendering and Superresolution](https://mlsanity.com/paper/2108.03918/) (2021)
- [Light Field Super-Resolution Via Graph-Based Regularization](https://mlsanity.com/paper/1701.02141/) (2017)
- [DeOccNet: Learning to See Through Foreground Occlusions in Light Fields](https://mlsanity.com/paper/1912.04459/) (2019)
- [High-Order Residual Network for Light Field Super-Resolution](https://mlsanity.com/paper/2003.13094/) (2020)

## Built on by (most cited first, 7 of 7)

- [Criteria Comparative Learning for Real-scene Image Super-Resolution](https://mlsanity.com/paper/2207.12767/) (2022)
- [NTIRE 2023 Challenge on Light Field Image Super-Resolution: Dataset, Methods and Results](https://mlsanity.com/paper/2304.10415/) (2023)
- [Global Priors Guided Modulation Network for Joint Super-Resolution and Inverse Tone-Mapping](https://mlsanity.com/paper/2208.06885/) (2022)
- [Deep Learning Empowered Super-Resolution: A Comprehensive Survey and Future Prospects](https://mlsanity.com/paper/2509.22692/) (2025)
- [Physics-Informed Ensemble Representation for Light-Field Image Super-Resolution](https://mlsanity.com/paper/2305.20006/) (2023)
- [Exploring Non-Local Spatial-Angular Correlations with a Hybrid Mamba-Transformer Framework for Light Field Super-Resolution](https://mlsanity.com/paper/2509.04824/) (2025)
- [History-Augmented Contrastive Learning With Soft Mixture of Experts for Blind Super-Resolution of Planetary Remote Sensing Images](https://mlsanity.com/paper/2511.20045/) (2025)

## Abstract (arXiv)

Light field (LF) images can be used to improve the performance of image super-resolution (SR) because both angular and spatial information is available. It is challenging to incorporate distinctive information from different views for LF image SR. Moreover, the long-term information from the previous layers can be weakened as the depth of network increases. In this paper, we propose a dense dual-attention network for LF image SR. Specifically, we design a view attention module to adaptively capture discriminative features across different views and a channel attention module to selectively focus on informative information across all channels. These two modules are fed to two branches and stacked separately in a chain structure for adaptive fusion of hierarchical features and distillation of valid information. Meanwhile, a dense connection is used to fully exploit multi-level information. Extensive experiments demonstrate that our dense dual-attention mechanism can capture informative information across views and channels to improve SR performance. Comparative results show the advantage of our method over state-of-the-art methods on public datasets.