K-svd
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Image Denoising via Sparse and Redundant Representations over Learned Dictionaries
Runnnig the code
python image_denoising.py -i imagename -iter 500 -coeff 2 -n_comp 100
optional parameters
- -iter: number of iterations required to learn the dictionary
- -n_comp: number of components that the dictionary should contain
- -coeff: number of non zero coefficients in the sparse representation of the image
output
The output will give you 3 images, Orignal, Noisy and Reconstructed
Results: Deep

The above picture shows the distortion of the image Lena and its subsequent reconstruction using dictionary learning (a) shows the orignal image (b) shows the image after adding gaussian noise (c) Reconstructed Image