Awesome
LESRCNN
Lightweight Image Super-Resolution with Enhanced CNN（LESRCNN）is conducted by Chunwei Tian, Ruibin Zhuge, Zhihao Wu, Yong Xu, Wangmeng Zuo, Chen Chen and Chia-Wen Lin, and accepted by Knowledge-Based Systems (IF:8.139) in 2020. It is implemented by Pytorch. And it is reported by Cver and 52CV. Its website is https://mp.weixin.qq.com/s/njlAEQXxjXKqFcxM7KYiqA. Its codes has been converted as CoreML format (for IOS) by the Japan engineer, where its link is https://github.com/john-rocky/CoreML-Models/blob/master/README.md#lesrcnn.
This paper uses a flexible sub-pixel convolutional technique for image blind super-resolution, which is useful for phones and cameras. Also, it has less parameters and faster super-resolution speed.
https://user-images.githubusercontent.com/25679314/195232308-d6883b2c-d3e0-4c03-9f64-3969e67e3a98.mp4
Abstract
Deep convolutional neural networks (CNNs) with strong expressive ability have achieved impressive performances on single image super-resolution (SISR). However, their excessive amounts ofconvolutions and parameters usually consume high computational cost and more memory storagefor training a SR model, which limits their applications to SR with resource-constrained devicesin real world. To resolve these problems, we propose a lightweight enhanced SR CNN (LESRCNN) with three successive sub-blocks, an information extraction and enhancement block (IEEB), a reconstruction block (RB) and an information refinement block (IRB). Specifically, the IEEB extracts hierarchical low-resolution (LR) features and aggregates the obtained features step-by-step to increase the memory ability of the shallow layers on deep layers for SISR. To remove redundant information obtained, a heterogeneous architecture is adopted in the IEEB. After that, the RB converts low-frequency features into high-frequency features by fusing global and local features, which is complementary with the IEEB in tackling the long-term dependency problem. Finally,the IRB uses coarse high-frequency features from the RB to learn more accurate SR features and construct a SR image. The proposed LESRCNN can obtain a high-quality image by a model fordifferent scales. Extensive experiments demonstrate that the proposed LESRCNN outperforms state-of-the-arts on SISR in terms of qualitative and quantitative evaluation.
Requirements (Pytorch)
Pytorch 0.41
Python 2.7
torchvision
openCv for Python
HDF5 for Python
Numpy, Scipy
Pillow, Scikit-image
importlib
Commands
Training datasets
Test datasets
preprocessing
cd dataset
python div2h5.py
Training a model for single scale
x2
python x2/train.py --patch_size 64 --batch_size 64 --max_steps 600000 --decay 400000 --model lesrcnn --ckpt_name lesrcnn_x2 --ckpt_dir checkpoint/lesrcnn_x2 --scale 2 --num_gpu 1
x3
python x3/train.py --patch_size 64 --batch_size 64 --max_steps 600000 --decay 400000 --model lesrcnn --ckpt_name lesrcnn_x3 --ckpt_dir checkpoint/lesrcnn_x3 --scale 3 --num_gpu 1
x4
python x4/train.py --patch_size 64 --batch_size 64 --max_steps 600000 --decay 400000 --model lesrcnn --ckpt_name lesrcnn_x4 --ckpt_dir checkpoint/lesrcnn_x4 --scale 4 --num_gpu 1
Training a model for different scales (also regarded as blind SR)
python lesrcnn_b/train.py --patch_size 64 --batch_size 64 --max_steps 600000 --decay 400000 --model lesrcnn --ckpt_name lesrcnn --ckpt_dir checkpoint/lesrcnn --scale 0 --num_gpu 1
Test
Single SR mode for x2
python x2/tcw_sample.py --model lesrcnn --test_data_dir dataset/Urban100 --scale 2 --ckpt_path ./x2/lesrcnn_x2.pth --sample_dir samples_singlemodel_urban100_x2
Single SR model for x3
python x3/tcw_sample.py --model lesrcnn --test_data_dir dataset/Urban100 --scale 3 --ckpt_path ./x3/lesrcnn_x3.pth --sample_dir samples_singlemodel_urban100_x3
Single SR model for x4
python x4/tcw_sample.py --model lesrcnn --test_data_dir dataset/Urban100 --scale 4 --ckpt_path ./x4/lesrcnn_x4.pth --sample_dir samples_singlemodel_urban100_x4
Using a model to test different scales of 2,3 and 4 (also regarded as blind SR)
python lesrcnn_b/tcw_sample_b.py --model lesrcnn --test_data_dir dataset/Urban100 --scale 2 --ckpt_path lesrcnn_b/lesrcnn.pth --sample_dir samples_singlemodel_urban100_x2
python lesrcnn_b/tcw_sample_b.py --model lesrcnn --test_data_dir dataset/Urban100 --scale 3 --ckpt_path lesrcnn_b/lesrcnn.pth --sample_dir samples_singlemodel_urban100_x3
python lesrcnn_b/tcw_sample_b.py --model lesrcnn --test_data_dir dataset/Urban100 --scale 4 --ckpt_path lesrcnn_b/lesrcnn.pth --sample_dir samples_singlemodel_urban100_x4
The Network architecture, principle and results of LESRCNN
1. Network architecture of LESRCNN.
2. Varying scales for upsampling operations.
3. Effectivenss of key components of LESRCNN.
4. Running time of key components of LESRCNN.
5. Complexity of key components of LESRCNN.
6. LESRCNN for x2, x3 and x4 on Set5.
7. LESRCNN for x2, x3 and x4 on Set14.
8. LESRCNN for x2, x3 and x4 on B100.
9. LESRCNN for x2, x3 and x4 on U100.
9. Running time of different methods on hr images of size 256x256, 512x512 and 1024x1024 for x2.
10. Complexities of different methods for x2.
11. Visual results of U100 for x2.
12. Visual results of Set14 for x3.
13. Visual results of B100 for x4.
If you cite this paper, please the following format:
1.Tian C, Zhuge R, Wu Z, et al. Lightweight image super-resolution with enhanced CNN[J]. Knowledge-Based Systems, 2020: 106235.
2.@article{tian2020lightweight,
title={Lightweight Image Super-Resolution with Enhanced CNN},
author={Tian, Chunwei and Zhuge, Ruibin and Wu, Zhihao and Xu, Yong and Zuo, Wangmeng and Chen, Chen and Lin, Chia-Wen},
journal={Knowledge-Based Systems},
pages={106235},
year={2020},
publisher={Elsevier}
}