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Check QMRNet's Remote Sensing article and iquaflow's JSTARS article for further documentation. You also can install iquaflow with pip and look at the iquaflow's wiki.

IQUAFLOW - QMRNet for Benchmarking Image Super-Resolution

The Single Image Super Resolution (SISR) use case is build to compare the image quality between different SiSR solutions. A SiSR algorithm inputs one frame and outputs an image with greater resolution. These are the methods that are being compared in the use case:

  1. Fast Super-Resolution Convolutional Neural Network (FSRCNN)
  2. Super-Resolution Using a Generative Adversarial Network (SRGAN)
  3. Multi-scale Residual Network (MSRN)
  4. Enhanced Super-Resolution Generative Adversarial Network (ESRGAN)
  5. Local Implicit Image Function (LIIF)
  6. Content Adaptive Resampler (CAR)

A use case in IQF usally involves wrapping a training within mlflow framework. In this case we estimate quality on the solutions offered by the different Dataset Modifiers which are the SISR algorithms. Similarity metrics against the Ground Truth are then compared.

To reproduce the experiments:

  1. git clone https://YOUR_GIT_TOKEN@github.com/dberga/iquaflow-qmr-sisr.git
  2. cd iquaflow-qmr-sisr
  3. Then build the docker image with make build.(***) This will also download required datasets and weights.
  4. In order to execute the experiments:
    • make dockershell (*)
    • Inside the docker terminal execute python ./iqf-usecase.py
  5. Start the mlflow server by doing make mlflow (*)
  6. Notebook examples can be launched and executed by make notebookshell NB_PORT=[your_port]" (**)
  7. To access the notebook from your browser in your local machine you can do:
    • If the executions are launched in a server, make a tunnel from your local machine. ssh -N -f -L localhost:[your_port]:localhost:[your_port] [remote_user]@[remote_ip] Otherwise skip this step.
    • Then, in your browser, access: localhost:[your_port]/?token=sisr

Notes

Note: make sure to replace "YOUR_GIT_TOKEN" to your github access token, also in Dockerfile.

Design and Train the QMRNet (regressor.py)

In QMRNet standalone code you can find several scripts for training and testing the QMRNet, mainly integrated in regressor.py. Using run_spec.sh you can specify any of the cfgs\ folder where the architecture design and hyperparameters are defined. You can create new versions by adding new .cfg files.

Cite

If you use content of this repo, please cite:

@article{berga2023,
AUTHOR = {Berga, David and Gallés, Pau and Takáts, Katalin and Mohedano, Eva and Riordan-Chen, Laura and Garcia-Moll, Clara and Vilaseca, David and Marín, Javier},
TITLE = {QMRNet: Quality Metric Regression for EO Image Quality Assessment and Super-Resolution},
JOURNAL = {Remote Sensing},
VOLUME = {15},
YEAR = {2023},
NUMBER = {9},
ARTICLE-NUMBER = {2451},
URL = {https://www.mdpi.com/2072-4292/15/9/2451},
ISSN = {2072-4292},
ABSTRACT = {The latest advances in super-resolution have been tested with general-purpose images such as faces, landscapes and objects, but mainly unused for the task of super-resolving earth observation images. In this research paper, we benchmark state-of-the-art SR algorithms for distinct EO datasets using both full-reference and no-reference image quality assessment metrics. We also propose a novel Quality Metric Regression Network (QMRNet) that is able to predict the quality (as a no-reference metric) by training on any property of the image (e.g., its resolution, its distortions, etc.) and also able to optimize SR algorithms for a specific metric objective. This work is part of the implementation of the framework IQUAFLOW, which has been developed for the evaluation of image quality and the detection and classification of objects as well as image compression in EO use cases. We integrated our experimentation and tested our QMRNet algorithm on predicting features such as blur, sharpness, snr, rer and ground sampling distance and obtained validation medRs below 1.0 (out of N = 50) and recall rates above 95%. The overall benchmark shows promising results for LIIF, CAR and MSRN and also the potential use of QMRNet as a loss for optimizing SR predictions. Due to its simplicity, QMRNet could also be used for other use cases and image domains, as its architecture and data processing is fully scalable.},
DOI = {10.3390/rs15092451}
}
@article{galles2024,
  title = {A New Framework for Evaluating Image Quality Including Deep Learning Task Performances as a Proxy},
  volume = {17},
  ISSN = {2151-1535},
  url = {https://ieeexplore.ieee.org/document/10356628},
  DOI = {10.1109/jstars.2023.3342475},
  journal = {IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing},
  publisher = {Institute of Electrical and Electronics Engineers (IEEE)},
  author = {Gallés,  Pau and Takáts,  Katalin and Hernández-Cabronero,  Miguel and Berga,  David and Pega,  Luciano and Riordan-Chen,  Laura and Garcia,  Clara and Becker,  Guillermo and Garriga,  Adan and Bukva,  Anica and Serra-Sagristà,  Joan and Vilaseca,  David and Marín,  Javier},
  year = {2024},
  pages = {3285–3296}
}