# Single Image Super-Resolution with Dilated Convolution based Multi-Scale Information Learning Inception Module

arXiv: https://arxiv.org/abs/1707.07128 · HTML: https://mlsanity.com/paper/1707.07128/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Jul 2017.

## Built on by (most cited first, 4 of 4)

- [Dilated Inception U-Net (DIU-Net) for Brain Tumor Segmentation](https://mlsanity.com/paper/2108.06772/) (2021)
- [EcoFlow: Efficient Convolutional Dataflows for Low-Power Neural Network Accelerators](https://mlsanity.com/paper/2202.02310/) (2022)
- [Subjective and Objective Quality Evaluation of Super-Resolution Enhanced Broadcast Images on a Novel SR-IQA Dataset](https://mlsanity.com/paper/2409.17451/) (2024)
- [A War Beyond Deepfake: Benchmarking Facial Counterfeits and Countermeasures](https://mlsanity.com/paper/2111.12912/) (2021)

## Abstract (arXiv)

Traditional works have shown that patches in a natural image tend to redundantly recur many times inside the image, both within the same scale, as well as across different scales. Make full use of these multi-scale information can improve the image restoration performance. However, the current proposed deep learning based restoration methods do not take the multi-scale information into account. In this paper, we propose a dilated convolution based inception module to learn multi-scale information and design a deep network for single image super-resolution. Different dilated convolution learns different scale feature, then the inception module concatenates all these features to fuse multi-scale information. In order to increase the reception field of our network to catch more contextual information, we cascade multiple inception modules to constitute a deep network to conduct single image super-resolution. With the novel dilated convolution based inception module, the proposed end-to-end single image super-resolution network can take advantage of multi-scale information to improve image super-resolution performance. Experimental results show that our proposed method outperforms many state-of-the-art single image super-resolution methods.