# Advanced Super-Resolution using Lossless Pooling Convolutional Networks

arXiv: https://arxiv.org/abs/1812.06023 · HTML: https://mlsanity.com/paper/1812.06023/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Dec 2018.

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

- [ShuffleUNet: Super resolution of diffusion-weighted MRIs using deep learning](https://mlsanity.com/paper/2102.12898/) (2021)

## Abstract (arXiv)

In this paper, we present a novel deep learning-based approach for still image super-resolution, that unlike the mainstream models does not rely solely on the input low resolution image for high quality upsampling, and takes advantage of a set of artificially created auxiliary self-replicas of the input image that are incorporated in the neural network to create an enhanced and accurate upscaling scheme. Inclusion of the proposed lossless pooling layers, and the fusion of the input self-replicas enable the model to exploit the high correlation between multiple instances of the same content, and eventually result in significant improvements in the quality of the super-resolution, which is confirmed by extensive evaluations.