# Improved Super-Resolution Convolution Neural Network for Large Images

arXiv: https://arxiv.org/abs/1907.12928 · HTML: https://mlsanity.com/paper/1907.12928/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Jul 2019.

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

- [Urban precipitation downscaling using deep learning: a smart city application over Austin, Texas, USA](https://mlsanity.com/paper/2209.06848/) (2022)

## Abstract (arXiv)

Single image super-resolution (SISR) is a very popular topic nowadays, which has both research value and practical value. In daily life, we crop a large image into sub-images to do super-resolution and then merge them together. Although convolution neural network performs very well in the research field, if we use it to do super-resolution, we can easily observe cutting lines from merged pictures. To address these problems, in this paper, we propose a refined architecture of SRCNN with 'Symmetric padding', 'Random learning' and 'Residual learning'. Moreover, we have done a lot of experiments to prove our model performs best among a lot of the state-of-art methods.