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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

The training dataset is downloaded at https://pan.baidu.com/s/1uqdUsVjnwM_6chh3n46CqQ (secret code:auh1)(baiduyun) or https://drive.google.com/file/d/1TNZeV0pkdPlYOJP1TdWvu5uEroH-EmP8/view (google drive)

Test datasets

The test dataset of Set5 is downloaded at 链接:https://pan.baidu.com/s/1YqoDHEb-03f-AhPIpEHDPQ (secret code:atwu) (baiduyun) or https://drive.google.com/file/d/1hlwSX0KSbj-V841eESlttoe9Ew7r-Iih/view?usp=sharing (google drive)

The test dataset of Set14 is downloaded at 链接:https://pan.baidu.com/s/1GnGD9elL0pxakS6XJmj4tA (secret code:vsks) (baiduyun) or https://drive.google.com/file/d/1us_0sLBFxFZe92wzIN-r79QZ9LINrxPf/view?usp=sharing (google drive)

The test dataset of B100 is downloaded at 链接:https://pan.baidu.com/s/1GV99jmj2wrEEAQFHSi8jWw (secret code:fhs2) (baiduyun) or https://drive.google.com/file/d/1G8FCPxPEVzaBcZ6B-w-7Mk8re2WwUZKl/view?usp=sharing (google drive)

The test dataset of Urban100 is downloaded at 链接:https://pan.baidu.com/s/15k55SkO6H6A7zHofgHk9fw (secret code:2hny) (baiduyun) or https://drive.google.com/file/d/1yArL2Wh79Hy2i7_YZ8y5mcdAkFTK5HOU/view?usp=sharing (google drive)

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.

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2. Varying scales for upsampling operations.

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3. Effectivenss of key components of LESRCNN.

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4. Running time of key components of LESRCNN.

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5. Complexity of key components of LESRCNN.

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6. LESRCNN for x2, x3 and x4 on Set5.

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7. LESRCNN for x2, x3 and x4 on Set14.

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8. LESRCNN for x2, x3 and x4 on B100.

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9. LESRCNN for x2, x3 and x4 on U100.

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9. Running time of different methods on hr images of size 256x256, 512x512 and 1024x1024 for x2.

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10. Complexities of different methods for x2.

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11. Visual results of U100 for x2.

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12. Visual results of Set14 for x3.

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13. Visual results of B100 for x4.

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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}

}