# Distilling with Residual Network for Single Image Super Resolution

## TL;DR

This paper introduces a residual distilling network (DRN) for single image super resolution that efficiently extracts features using residual distilling blocks and groups, achieving superior performance with smaller models.

## Contribution

The paper proposes a novel residual distilling block and group architecture that enhances feature extraction efficiency for SISR, reducing model size while improving performance.

## Key findings

- DRN outperforms state-of-the-art methods on benchmark datasets.
- DRN achieves a better trade-off between performance and model size.
- The proposed architecture effectively fuses local and global features.

## Abstract

Recently, the deep convolutional neural network (CNN) has made remarkable progress in single image super resolution(SISR). However, blindly using the residual structure and dense structure to extract features from LR images, can cause the network to be bloated and difficult to train. To address these problems, we propose a simple and efficient distilling with residual network(DRN) for SISR. In detail, we propose residual distilling block(RDB) containing two branches, while one branch performs a residual operation and the other branch distills effective information. To further improve efficiency, we design residual distilling group(RDG) by stacking some RDBs and one long skip connection, which can effectively extract local features and fuse them with global features. These efficient features beneficially contribute to image reconstruction. Experiments on benchmark datasets demonstrate that our DRN is superior to the state-of-the-art methods, specifically has a better trade-off between performance and model size.