# Image Super-Resolution Using T-Tetromino Pixels

arXiv: https://arxiv.org/abs/2111.09013 · HTML: https://mlsanity.com/paper/2111.09013/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 52 references that are in the map.
- First posted on arXiv 17 Nov 2021.

## Builds on (most cited first, 5 of 5)

- [Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification](https://mlsanity.com/paper/1502.01852/) (2015)
- [Image Super-Resolution Using Deep Convolutional Networks](https://mlsanity.com/paper/1501.00092/) (2014)
- [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)
- [Toward Bridging the Simulated-to-Real Gap: Benchmarking Super-Resolution on Real Data](https://mlsanity.com/paper/1809.06420/) (2018)

## Built on by (most cited first, 2 of 2)

- [Minimalist Vision with Freeform Pixels](https://mlsanity.com/paper/2501.00142/) (2024)
- [Cross-Modal Pre-Aligned Method with Global and Local Information for Remote-Sensing Image and Text Retrieval](https://mlsanity.com/paper/2411.14704/) (2024)

## Abstract (arXiv)

For modern high-resolution imaging sensors, pixel binning is performed in low-lighting conditions and in case high frame rates are required. To recover the original spatial resolution, single-image super-resolution techniques can be applied for upscaling. To achieve a higher image quality after upscaling, we propose a novel binning concept using tetromino-shaped pixels. It is embedded into the field of compressed sensing and the coherence is calculated to motivate the sensor layouts used. Next, we investigate the reconstruction quality using tetromino pixels for the first time in literature. Instead of using different types of tetrominoes as proposed elsewhere, we show that using a small repeating cell consisting of only four T-tetrominoes is sufficient. For reconstruction, we use a locally fully connected reconstruction (LFCR) network as well as two classical reconstruction methods from the field of compressed sensing. Using the LFCR network in combination with the proposed tetromino layout, we achieve superior image quality in terms of PSNR, SSIM, and visually compared to conventional single-image super-resolution using the very deep super-resolution (VDSR) network. For PSNR, a gain of up to SI[retain-explicit-plus]+1.92dB is achieved.