DistNet: Generating Image Distortion Maps Using Convolutional Autoencoders with Application to No Reference Image Quality Assessment

You can find full access to our paper here. In this post, we provides the trained models and example code for generating image distortion map from input natural image.

-Network architecture of the proposed DistNet as follows

Citation:

If you are using the code/model/data provided here in a publication, please cite our paper:

@ARTICLE{8521680,
author={S. V. R. {Dendi} and C. {Dev} and N. {Kothari} and S. S. {Channappayya}},
journal={IEEE Signal Processing Letters},
title={Generating Image Distortion Maps Using Convolutional Autoencoders With Application to No Reference Image Quality Assessment},
year={2019},
volume={26},
number={1},
pages={89-93},
doi={10.1109/LSP.2018.2879518},
ISSN={1070-9908},
month={Jan},}

Pretrained models and example codes:

Without MSCN: pretrained model and example code for generating distortion map.

With MSCN: pretrained model and example code for generating distortion map.

Install prerequisites:

Keras: https://keras.io/#installation