laoyangui / RDRN

This project is for RDRN introduced in the following paper "Lightweight Feature Rotated Distillation Residual Network for Single Image Super-Resolution".

Lightweight Feature Rotated Distillation Residual Network for Single Image Super-Resolution

This project is for RDRN introduced in the following paper "Lightweight Feature Rotated Distillation Residual Network for Single Image Super-Resolution", submitted to IEEE TCSVT.

The code is test on Ubuntu 16.04 environment (Python3.6, PyTorch >= 1.1.0) with Nvidia 1070 Ti GPUs.

Contents

1. Introduction
2. Test
3. Acknowledgements

Introduction

Deep convolutional neural networks (CNNs) have achieved great success in single image super-resolution (SISR). However, modern state-of-the-art SISR networks often require high computational resources beyond the capabilities of many devices. Therefore, it is valuable to design a lightweight SISR network with excellent reconstruction quality. In this paper, a lightweight feature rotated distillation residual network (RDRN) is proposed for SISR. Our lightweight RDRN is built on a distillation residual backbone, and the construction of RDRN is considered from both spatial and channel domains. In spatial domains, we propose a feature rotation (FR) strategy to capture geometric self-ensemble information with negligible extra computation and show that the FR strategy can achieve comparable SR performance to the geometric self-ensemble. Furthermore, we propose a lightweight spatial feature extraction block, called small kernel block (SKB), which uses small granularity kernels to reduce computational complexity. In channel domains, we propose an efficient attention mechanism contrast-aware efficient channel attention (CECA), which can improve SR performance with low computational complexity by providing a local cross-channel interaction without dimensionality reduction. Experimental results demonstrate that the proposed RDRN can achieve state-of-the-art results for lightweight SISR in terms of both restoration quality and model complexity.

Lightweight feature rotated distillation residual network (RDRN).

Test

Quick start

1. Our Trained RDRN models are located at './checkpoints'.
2. The test data Set5 is stored in './Test_Datasets'.
3. Run 'main_test_RDRN.py' script to generate SR results.
4. Run 'Evaluate_PSNR_SSIM.m' script to evaluate psnr and ssim. (matlab R2018b)

Acknowledgements

This code is built on EDSR (PyTorch). We thank the authors for sharing their codes.