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

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

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

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

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

Examples (deblurring) blurry input deblurred output

Examples (deblurring) blurry input deblurred output

Examples (denoising) noisy input denoised output

Examples (denoising) noisy input denoised output

Examples (sharpening) input image output image

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. 5136-5151, Dec. 2014. [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.

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