1. A Graph-based Framework for Image Restoration
Amin Kheradmand, Peyman Milanfar
Department of Electrical Engineering
University of California, Santa Cruz

2. Restoration algorithm
Motivation
With existing hand held cameras, degradations in the form of noise/blur in the captured images are inevitable.
Restoration algorithm
input image
enhanced output

3. kernel similarity matrix
Motivation
Graph-based representation is an effective way for describing the underlying structure of images.
kernel similarity matrix

4. Contributions
We have developed a general regularization framework based on a new definition of the graph Laplacian for different restoration problems.
blurry input
deblurred output

5. Contributions We have proposed a data-adaptive sharpening algorithm.
input image
output image

6. Examples (deblurring)
blurry input
deblurred output

7. Examples (deblurring)
blurry input
deblurred output

8. Examples (denoising)
noisy input
denoised output

9. Examples (denoising)
noisy input
denoised output

10. Examples (sharpening)
input image
output image

11. References
[1] A. Kheradmand, and P. Milanfar, “Non-linear structure-aware image sharpening with difference of smoothing operators”, Frontiers in ICT, Computer Image Analysis, vol. 2, no. 22, 2015.
[2] A. Kheradmand, and P. Milanfar, “A general framework for regularized, similarity-based image restoration”, IEEE Transactions on Image Processing, vol. 23, No. 12, pp , Dec
[3] A. Kheradmand and P. Milanfar, “Motion deblurring with graph Laplacian regularization”, Digital Photography and Mobile Imaging XI conference, IS&T/SPIE Electronic Imaging 2015, San Francisco, CA.
[4] A. Kheradmand, and P. Milanfar, “A general framework for kernel similarity-based image denoising,” IEEE Global Conference on Signal and Information Processing (GlobalSIP), Dec. 2013, Austin, TX.

12. Thanks for your time.