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Consider an image which is corrupted by both additive Gaussian noise and defocus blur. Here I have implemented a Wiener filter to restore the image to make it less noisy and less blurry. To evaluate the restored image I use PSNR (Peak Signal to Noise Ratio).

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Name		Name	Last commit message	Last commit date
Latest commit History 6 Commits
bwdataset		bwdataset
README.md		README.md
apply_wiener_filter.m		apply_wiener_filter.m
final_func.m		final_func.m
report.pdf		report.pdf
train_for_SNR.m		train_for_SNR.m

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Wiener Filter

The repository contains 3 MATLAB scripts

final_func.m
train_for_SNR.m
apply_wiener_filter.m

Note :

Set the dataset_dir variable to the path of directory named bwdataset. You might need to specify the path with respect to the root directory.
Set test_img variable to name of image you want to test. Do not specify the path, only name.

How to Run?

Run final_func.m script in MATLAB.

Output format

There are 2 outputs

PSNR values of corrupted image and restored image are printed in console.
The original image, corrupted image and restored images are shown in another window.

For details on dataset used and results, please refer to report.pdf

This work was done as part of assignment in the Probability and Random Processes course at IIT Gandhinagar

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Consider an image which is corrupted by both additive Gaussian noise and defocus blur. Here I have implemented a Wiener filter to restore the image to make it less noisy and less blurry. To evaluate the restored image I use PSNR (Peak Signal to Noise Ratio).

matlab psnr wiener-filter restored-images guassian-noise

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