# MaskBlur: Spatial and Angular Data Augmentation for Light Field Image Super-Resolution

arXiv: https://arxiv.org/abs/2410.06478 · HTML: https://mlsanity.com/paper/2410.06478/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 35 of its 73 references that are in the map.
- First posted on arXiv 9 Oct 2024.

## Builds on (most cited first, 30 of 35)

- [Masked Autoencoders Are Scalable Vision Learners](https://mlsanity.com/paper/2111.06377/) (2021)
- [mixup: Beyond Empirical Risk Minimization](https://mlsanity.com/paper/1710.09412/) (2017)
- [CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features](https://mlsanity.com/paper/1905.04899/) (2019)
- [BEiT: BERT Pre-Training of Image Transformers](https://mlsanity.com/paper/2106.08254/) (2021)
- [Improved Regularization of Convolutional Neural Networks with Cutout](https://mlsanity.com/paper/1708.04552/) (2017)
- [AutoAugment: Learning Augmentation Policies from Data](https://mlsanity.com/paper/1805.09501/) (2018)
- [Bag of Tricks for Image Classification with Convolutional Neural Networks](https://mlsanity.com/paper/1812.01187/) (2018)
- [Deep Unfolding Network for Image Super-Resolution](https://mlsanity.com/paper/2003.10428/) (2020)
- [LLNet: A Deep Autoencoder Approach to Natural Low-light Image Enhancement](https://mlsanity.com/paper/1511.03995/) (2015)
- [FractalNet: Ultra-Deep Neural Networks without Residuals](https://mlsanity.com/paper/1605.07648/) (2016)
- [Hide-and-Seek: Forcing a Network to be Meticulous for Weakly-supervised Object and Action Localization](https://mlsanity.com/paper/1704.04232/) (2017)
- [Blind Super-Resolution With Iterative Kernel Correction](https://mlsanity.com/paper/1904.03377/) (2019)
- [Blind Super-Resolution Kernel Estimation using an Internal-GAN](https://mlsanity.com/paper/1909.06581/) (2019)
- [Unfolding the Alternating Optimization for Blind Super Resolution](https://mlsanity.com/paper/2010.02631/) (2020)
- [Shake-Shake regularization](https://mlsanity.com/paper/1705.07485/) (2017)
- [Rethinking Data Augmentation for Image Super-resolution: A Comprehensive Analysis and a New Strategy](https://mlsanity.com/paper/2004.00448/) (2020)
- [Seven ways to improve example-based single image super resolution](https://mlsanity.com/paper/1511.02228/) (2015)
- [Bag of Freebies for Training Object Detection Neural Networks](https://mlsanity.com/paper/1902.04103/) (2019)
- [EPINET: A Fully-Convolutional Neural Network Using Epipolar Geometry for Depth from Light Field Images](https://mlsanity.com/paper/1804.02379/) (2018)
- [Disentangling Light Fields for Super-Resolution and Disparity Estimation](https://mlsanity.com/paper/2202.10603/) (2022)
- [ShakeDrop Regularization for Deep Residual Learning](https://mlsanity.com/paper/1802.02375/) (2018)
- [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)
- [Light Field Image Super-Resolution with Transformers](https://mlsanity.com/paper/2108.07597/) (2021)
- [Detail-Preserving Transformer for Light Field Image Super-Resolution](https://mlsanity.com/paper/2201.00346/) (2022)
- [Occlusion-Aware Cost Constructor for Light Field Depth Estimation](https://mlsanity.com/paper/2203.01576/) (2022)
- [Learning Non-Local Spatial-Angular Correlation for Light Field Image Super-Resolution](https://mlsanity.com/paper/2302.08058/) (2023)
- [Light Field Super-Resolution Via Graph-Based Regularization](https://mlsanity.com/paper/1701.02141/) (2017)
- [Learning Sub-Pixel Disparity Distribution for Light Field Depth Estimation](https://mlsanity.com/paper/2208.09688/) (2022)
- [LRT: An Efficient Low-Light Restoration Transformer for Dark Light Field Images](https://mlsanity.com/paper/2209.02197/) (2022)

## Built on by (most cited first, 1 of 1)

- [Physically Consistent Image Augmentation for Deep Learning in Mueller Matrix Polarimetry](https://mlsanity.com/paper/2411.07918/) (2024)

## Abstract (arXiv)

Data augmentation (DA) is an effective approach for enhancing model performance with limited data, such as light field (LF) image super-resolution (SR). LF images inherently possess rich spatial and angular information. Nonetheless, there is a scarcity of DA methodologies explicitly tailored for LF images, and existing works tend to concentrate solely on either the spatial or angular domain. This paper proposes a novel spatial and angular DA strategy named MaskBlur for LF image SR by concurrently addressing spatial and angular aspects. MaskBlur consists of spatial blur and angular dropout two components. Spatial blur is governed by a spatial mask, which controls where pixels are blurred, i.e., pasting pixels between the low-resolution and high-resolution domains. The angular mask is responsible for angular dropout, i.e., selecting which views to perform the spatial blur operation. By doing so, MaskBlur enables the model to treat pixels differently in the spatial and angular domains when super-resolving LF images rather than blindly treating all pixels equally. Extensive experiments demonstrate the efficacy of MaskBlur in significantly enhancing the performance of existing SR methods. We further extend MaskBlur to other LF image tasks such as denoising, deblurring, low-light enhancement, and real-world SR. Code is publicly available at urlhttps://github.com/chaowentao/MaskBlur.