Brief:
Project uses visual comparisions mainly based on DX and all-MIAS dataset, comparing outputs with CNN Autoencoder results, BM3D and NL means denoising algorithm
Comparisions are based on structural similarity index measure(SSIM) instead of peak signal to noise ratio (PSNR) for its consistency and accuracy.
A composite index of three measures, SSIM estimates the visual effects of shifts in image luminance, contrast and other remaining errors, collectively called structural changes.
*Different Gaussian noise level are used to add noise to the image.
*Datasets DX and MIAS are used as default datasets
*They should be placed in data folder:
*/data/dx
*/data/all-mias
Multiple samples of different outputs can be found in Samples folder
For more information how to run please use main.py -h
Project based on Medical image denoising using convolutional denoising autoencoders by Lovedeep Gondara
Paper: https://arxiv.org/pdf/1608.04667.pdf
By Adam Mahameed
Sample:
.png?raw=true)
This code is under MIT License
Copyright (c) 2020 Adam Mahameed
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