This repository contains an op-for-op PyTorch reimplementation of Research and application of GAN based super resolution technology for pathological microscopic images.
- About Research and application of GAN based super resolution technology for pathological microscopic images
- Model Description
- Installation
- Script
- Test
- Train
- Contributing
- Credit
About Research and application of GAN based super resolution technology for pathological microscopic images
If you're new to SSRGAN, here's an abstract straight from the paper:
Despite the breakthroughs in accuracy and speed of single image super-resolution using faster and deeper convolutional neural networks, one central problem remains largely unsolved: how do we recover the finer texture details when we super-resolve at large upscaling factors? The behavior of optimization-based super-resolution methods is principally driven by the choice of the objective function. Recent work has largely focused on minimizing the mean squared reconstruction error. The resulting estimates have high peak signal-to-noise ratios, but they are often lacking high-frequency details and are perceptually unsatisfying in the sense that they fail to match the fidelity expected at the higher resolution. In this paper, we present SSRGAN, a generative adversarial network (GAN) for image super-resolution (SR). To our knowledge, it is the first framework capable of inferring photo-realistic natural images for 4x upscaling factors. To achieve this, we propose a perceptual loss function which consists of an adversarial loss and a content loss. The adversarial loss pushes our solution to the natural image manifold using a discriminator network that is trained to differentiate between the super-resolved images and original photo-realistic images. In addition, we use a content loss motivated by perceptual similarity instead of similarity in pixel space. Our deep residual network is able to recover photo-realistic textures from heavily downsampled images on public benchmarks. An extensive mean-opinion-score (MOS) test shows hugely significant gains in perceptual quality using SSRGAN. The MOS scores obtained with SSRGAN are closer to those of the original high-resolution images than to those obtained with any state-of-the-art method.
We have two networks, G (Generator) and D (Discriminator).The Generator is a network for generating images. It receives a random noise z and generates images from this noise, which is called G(z).Discriminator is a discriminant network that discriminates whether an image is real. The input is x, x is a picture, and the output is D of x is the probability that x is a real picture, and if it's 1, it's 100% real, and if it's 0, it's not real.
$ git clone https://github.com/Lornatang/SSRGAN.git
$ cd SSRGAN/
$ pip3 install -r requirements.txt$ cd weights/
$ python3 download_weights.py$ cd data/
$ bash download_dataset.sh$ python3 scripts/cal_model_complexity.py
Summary
-----------------------------------------------
| Model | Params | FLOPs |
-----------------------------------------------
| BioNet | 0.27M | 2.19GMac |
| ESRGAN | 16.92M | 52.35GMac |
| Inception | 0.88M | 7.10GMac |
| MobileNetV1 | 0.35M | 4.31GMac |
| MobileNetV2 | 1.78M | 11.37GMac |
| MobileNetV3 | 3.98M | 12.82GMac |
| RFBESRGAN | 21.31M | 66.49GMac |
| ShuffleNetV1 | 0.22M | 3.70GMac |
| ShuffleNetV2 | 0.25M | 3.87GMac |
| SqueezeNet | 0.87M | 6.93GMac |
| SRGAN | 1.54M | 6.46GMac |
| UNet | 2.36M | 8.87GMac |
-----------------------------------------------
Using pre training model to generate pictures.
usage: test_image.py [-h] --lr LR --hr HR [--outf PATH] [--device DEVICE]
[--detail] [-a ARCH] [--upscale-factor {4}]
[--model-path PATH] [--pretrained]
Research and application of GAN based super resolution technology for
pathological microscopic images.
optional arguments:
-h, --help show this help message and exit
--lr LR Test low resolution image name.
--hr HR Raw high resolution image name.
--outf PATH The location of the image in the evaluation process.
(default: ``test``).
--device DEVICE device id i.e. `0` or `0,1` or `cpu`. (default:
``cpu``).
--detail Use comprehensive assessment.
-a ARCH, --arch ARCH model architecture: bionet | esrgan |
get_upsample_filter | inception | lapsrn | mobilenetv1
| mobilenetv2 | mobilenetv3 | rfb_esrgan |
shufflenetv1 | shufflenetv2 | squeezenet | srgan |
unet (default: bionet)
--upscale-factor {4} Low to high resolution scaling factor. (default:4).
--model-path PATH Path to latest checkpoint for model. (default: ````).
--pretrained Use pre-trained model.
# Example
$ python3 test_image.py -a bionet --pretrained --lr <path>/<to>/lr.png --hr <path>/<to>/hr.png
usage: test_benchmark.py [-h] [--dataroot DATAROOT] [-j N] [--outf PATH]
[--device DEVICE] [--detail] [-a ARCH]
[--upscale-factor {4}] [--model-path PATH]
[--pretrained] [-b N]
Research and application of GAN based super resolution technology for
pathological microscopic images.
optional arguments:
-h, --help show this help message and exit
--dataroot DATAROOT Path to datasets. (default:`./data`)
-j N, --workers N Number of data loading workers. (default:4)
--outf PATH The location of the image in the evaluation process.
(default: ``test``).
--device DEVICE device id i.e. `0` or `0,1` or `cpu`. (default: ````).
--detail Use comprehensive assessment.
-a ARCH, --arch ARCH model architecture: bionet | esrgan |
get_upsample_filter | inception | lapsrn | mobilenetv1
| mobilenetv2 | mobilenetv3 | rfb_esrgan |
shufflenetv1 | shufflenetv2 | squeezenet | srgan |
unet (default: bionet)
--upscale-factor {4} Low to high resolution scaling factor. (default:4).
--model-path PATH Path to latest checkpoint for model. (default: ````).
--pretrained Use pre-trained model.
-b N, --batch-size N mini-batch size (default: 16), this is the total batch
size of all GPUs on the current node when using Data
Parallel or Distributed Data Parallel.
# Example
$ python3 test_benchmark.py -a bionet --pretrained
usage: test_video.py [-h] --file FILE [--outf PATH] [--device DEVICE] [--view]
[-a ARCH] [--upscale-factor {4}] [--model-path PATH]
[--pretrained]
Research and application of GAN based super resolution technology for
pathological microscopic images.
optional arguments:
-h, --help show this help message and exit
--file FILE Test low resolution video name.
--outf PATH The location of the image in the evaluation process.
(default: ``video``).
--device DEVICE device id i.e. `0` or `0,1` or `cpu`. (default:
``0``).
--view Super resolution real time to show.
-a ARCH, --arch ARCH model architecture: bionet | esrgan |
get_upsample_filter | inception | lapsrn | mobilenetv1
| mobilenetv2 | mobilenetv3 | rfb_esrgan |
shufflenetv1 | shufflenetv2 | squeezenet | srgan |
unet (default: bionet)
--upscale-factor {4} Low to high resolution scaling factor. (default:4).
--model-path PATH Path to latest checkpoint for model. (default: ````).
--pretrained Use pre-trained model.
# Example
$ python3 test_video.py -a bionet --pretrained --file <path>/<to>/video.mp4
Low resolution / Recovered High Resolution / Ground Truth
usage: train.py [-h] [--dataroot DATAROOT] [-j N] [--manualSeed MANUALSEED]
[--device DEVICE] [--save-freq SAVE_FREQ] [-a ARCH]
[--upscale-factor {4}] [--model-path PATH] [--pretrained]
[--resume-PSNR] [--resume] [--start-epoch N] [--psnr-iters N]
[--iters N] [-b N] [--psnr-lr PSNR_LR] [--lr LR]
Research and application of GAN based super resolution technology for
pathological microscopic images.
optional arguments:
-h, --help show this help message and exit
--dataroot DATAROOT Path to datasets. (default:`./data`)
-j N, --workers N Number of data loading workers. (default:4)
--manualSeed MANUALSEED
Seed for initializing training. (default:1111)
--device DEVICE device id i.e. `0` or `0,1` or `cpu`. (default: ````).
--save-freq SAVE_FREQ
frequency of evaluating and save the model.
-a ARCH, --arch ARCH model architecture: bionet | esrgan |
get_upsample_filter | inception | lapsrn | mobilenetv1
| mobilenetv2 | mobilenetv3 | rfb_esrgan |
shufflenetv1 | shufflenetv2 | squeezenet | srgan |
unet (default: bionet)
--upscale-factor {4} Low to high resolution scaling factor. (default:4).
--model-path PATH Path to latest checkpoint for model. (default: ````).
--pretrained Use pre-trained model.
--resume-PSNR Path to latest checkpoint for PSNR model.
--resume Path to latest checkpoint for Generator.
--start-epoch N manual epoch number (useful on restarts)
--psnr-iters N The number of iterations is needed in the training of
PSNR model. (default:1e6)
--iters N The training of srgan model requires the number of
iterations. (default:4e5)
-b N, --batch-size N mini-batch size (default: 8), this is the total batch
size of all GPUs on the current node when using Data
Parallel or Distributed Data Parallel.
--psnr-lr PSNR_LR Learning rate for PSNR model. (default:2e-4)
--lr LR Learning rate. (default:1e-4)
# Example (e.g DIV2K)
$ python3 train.py -a bionet
If you want to load weights that you've trained before, run the following command.
$ python3 train.py --resume-PSNRIf you find a bug, create a GitHub issue, or even better, submit a pull request. Similarly, if you have questions, simply post them as GitHub issues.
I look forward to seeing what the community does with these models!
Research and application of GAN based super resolution technology for pathological microscopic images
Changyu Liu, Qiyue Yu, Bo Wang, Yang Wang, Riliang Wu, Yahong Liu, Rundong Chen, Lanjing Xiao
Abstract
Despite the breakthroughs in accuracy and speed of single image super-resolution using faster and
deeper convolutional neural networks, one central problem remains largely unsolved: how do we recover
the finer texture details when we super-resolve at large upscaling factors? The behavior of
optimization-based super-resolution methods is principally driven by the choice of the objective function.
Recent work has largely focused on minimizing the mean squared reconstruction error.
The resulting estimates have high peak signal-to-noise ratios, but they are often lacking high-frequency details and
are perceptually unsatisfying in the sense that they fail to match the fidelity expected at the higher resolution.
In this paper, we present SSRGAN, a generative adversarial network (GAN) for image super-resolution (SR).
To our knowledge, it is the first framework capable of inferring photo-realistic natural images for 4x upscaling factors.
To achieve this, we propose a perceptual loss function which consists of an adversarial loss and a content loss.
The adversarial loss pushes our solution to the natural image manifold using a discriminator network that is trained
to differentiate between the super-resolved images and original photo-realistic images. In addition,
we use a content loss motivated by perceptual similarity instead of similarity in pixel space.
Our deep residual network is able to recover photo-realistic textures from heavily downsampled images on public benchmarks.
An extensive mean-opinion-score (MOS) test shows hugely significant gains in perceptual quality using SSRGAN.
The MOS scores obtained with SSRGAN are closer to those of the original high-resolution images than to those obtained
with any state-of-the-art method.
@InProceedings{ssrgan,
author = {Changyu Liu, Qiyue Yu, Bo Wang, Yang Wang, Riliang Wu, Yahong Liu, Rundong Chen, Lanjing Xiao},
title = {Research and application of GAN based super resolution technology for pathological microscopic images},
booktitle = {-},
year = {2020}
}