This is the source code for our paper: Classification-based Dynamic Network for Efficient Super-Resolution. A brief introduction of this work is as follows:
Deep neural networks (DNNs) based approaches have achieved superior performance in single image super-resolution (SR). To obtain better visual quality, DNNs for SR are generally designed with massive computation overhead. To accelerate network inference under resource constraints, we propose a classification-based dynamic network for efficient super-resolution (CDNSR), which combines the classification and SR networks in a unified framework. Specifically, CDNSR decomposes a large image into a number of image-patches, and uses a classification network to categorize them into different classes based on the restoration difficulty. Each class of image-patches will be handled by the SR network that corresponds to the difficulty of this class. In particular, we design a new loss to trade off between the computational overhead and the reconstruction quality. Besides, we apply contrastive learning based knowledge distillation to guarantee the performance of SR networks and the quality of reconstructed images. Extensive experiments show that CDNSR significantly outperforms the other SR networks and backbones on image quality and computational overhead.
基于深度神经网络（DNN）的方法在单图像超分辨率（SR）任务中已取得卓越性能。为获得更优的视觉质量，超分辨率神经网络通常被设计为具有巨大计算开销的结构。为在资源约束下加速网络推理，我们提出一种基于分类的动态超分辨率高效网络（CDNSR），将分类网络与超分辨率网络整合至统一框架。具体而言，CDNSR将大幅图像分解为若干图像块，并采用分类网络根据复原难度将其划分为不同类别。每类图像块将由与其难度相对应的超分辨率网络进行处理。我们特别设计了一种新型损失函数，用于权衡计算开销与重建质量。此外，应用基于对比学习的知识蒸馏技术来保证超分辨率网络的性能与重建图像的质量。大量实验表明，CDNSR在图像质量和计算开销方面显著优于其他超分辨率网络及骨干模型。
This paper has been published by ICASSP 2023, and can be accessed from IEEExplore. Due to the 5-page requirement of this conference, we provide a full version of technique report in this repo.
@inproceedings{wang2023classification,
title={Classification-Based Dynamic Network for Efficient Super-Resolution},
author={Wang, Qi and Fang, Weiwei and Wang, Meng and Cheng, Yusong},
booktitle={ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
pages={1--5},
year={2023},
organization={IEEE}
}
PyTorch
train
cd codes
python train_SR_Net.py -opt options/train/train_CARN_branch1.yml
python train_SR_Net.py -opt options/train/train_CARN_branch2.yml
python train_SR_Net.py -opt options/train/train_CARN_branch3.ymltest
cd codes
python test_SR_Net.py -opt options/test/test_CARN.yml
train
cd codes
python train_CDNSR.py -opt options/train/train_CDNSR_CARN.yml
distill
cd codes
python train_CDNSR.py -opt options/train/train_CDNSR_CARN_KD.yml
test
cd codes
python test_CDNSR.py -opt options/test/test_CDNSR_CARN.yml
Qi Wang (20120417@bjtu.edu.cn)
Please note that the open source code in this repository was mainly completed by the graduate student author during his master's degree study. Since the author did not continue to engage in scientific research work after graduation, it is difficult to continue to maintain and update these codes. We sincerely apologize that these codes are for reference only.