# Sub-Aperture Feature Adaptation in Single Image Super-resolution Model for Light Field Imaging

arXiv: https://arxiv.org/abs/2207.11894 · HTML: https://mlsanity.com/paper/2207.11894/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 26 references that are in the map.
- First posted on arXiv 25 Jul 2022.

## Builds on (most cited first, 8 of 8)

- [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)
- [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)
- [Spatial-Angular Interaction for Light Field Image Super-Resolution](https://mlsanity.com/paper/1912.07849/) (2019)
- [Light Field Image Super-Resolution Using Deformable Convolution](https://mlsanity.com/paper/2007.03535/) (2020)
- [Detail-Preserving Transformer for Light Field Image Super-Resolution](https://mlsanity.com/paper/2201.00346/) (2022)
- [Learning Light Field Angular Super-Resolution via a Geometry-Aware Network](https://mlsanity.com/paper/2002.11263/) (2020)

## Abstract (arXiv)

With the availability of commercial Light Field (LF) cameras, LF imaging has emerged as an up and coming technology in computational photography. However, the spatial resolution is significantly constrained in commercial microlens based LF cameras because of the inherent multiplexing of spatial and angular information. Therefore, it becomes the main bottleneck for other applications of light field cameras. This paper proposes an adaptation module in a pretrained Single Image Super Resolution (SISR) network to leverage the powerful SISR model instead of using highly engineered light field imaging domain specific Super Resolution models. The adaption module consists of a Sub aperture Shift block and a fusion block. It is an adaptation in the SISR network to further exploit the spatial and angular information in LF images to improve the super resolution performance. Experimental validation shows that the proposed method outperforms existing light field super resolution algorithms. It also achieves PSNR gains of more than 1 dB across all the datasets as compared to the same pretrained SISR models for scale factor 2, and PSNR gains 0.6 to 1 dB for scale factor 4.