IQA-Distortion-Classification-and-Reconstruction-System
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RainFZY
Image Quality Assessment, Image Distortion Classification and Image Reconstruction in a single Django Web system
IQA, Distortion Classification and Reconstruction System
IQA (Image Quality Assessment)
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Use multiple models, including CNN, LeNet-5, ResNet, VGG ... to respectively assesses the input image's quality and get the score. The final IQA score of the input image is the average of all these scores.
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Models are provided in the folder
app/assess/models -
To check the training process or get your own model, follow the link:
https://github.com/RainFZY/Image-Quality-Assessment-By-Multiple-Models
Image Distortion Classification
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Classify the distortion of input image into 3 classes:
noise (wn), blur (gblur), JPEG compression (jpeg)
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Provide each classification's confidence coefficient.
Image Reconstruction
- Restore a noise-labeled image to a higher-quality image by noise elimination
Start the Django Service
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Enter the project folder in cmd
cd ../IQA-and-Distortion-Classification-System -
run
python manage.py runserver -
Enter http://127.0.0.1:8000/ in your browser
Test Demo
Home page
Test a blurred image
The IQA scores and the distortion classification results are listed in the right area
Test a JPEG compression image
Test a noisy image
RainFZY