Regularized
Kernel Regression-Based Deblurring (AKTV) |
||||||||||||||
Hiroyuki Takeda, Dr. Sina Farsiu, and Prof. Peyman Milanfar |
||||||||||||||
Abstract |
||||||||||||||
Kernel regression is an effective tool for a variety of image processing tasks such as denoising and interpolation. In this work, we extended the use of kernel regression for deblurring applications. In some earlier examples in the literature, such non-parametric deblurring was sub-optimally performed in two sequential steps, namely, denoising followed by deblurring. In contrast, our optimal solution jointly denoises and deblurs images. The proposed algorithm takes advantage of an effective and novel image prior (Adaptive Kernel Total Variation -- AKTV) that generalizes some of the most popular regularization techniques in the literature. Full paper [PDF] (accepted for publication in IEEE Transactions on Image Processing) |
||||||||||||||
Examples |
||||||||||||||
Case 1: Large blur with a high SNR | ||||||||||||||
*2: Blurred Signal to Noise Ratio = 10 log (Blurred signal variance / Noise variance) [dB]. *3: Root Mean Square Error. |
||||||||||||||
Case 2: Large blur with a medium SNR | ||||||||||||||
|
||||||||||||||
Case 3: Small blur with a low SNR | ||||||||||||||
|
||||||||||||||
Case 4: A fair amount of blur and noise | ||||||||||||||
Software |
||||||||||||||
We've prepared the image deblurring toolbox for MATLAB that provides the experimental examples showed above. The latest version of the package can be downloaded from here. Instruction:
|
||||||||||||||
Acknowledgement |
||||||||||||||
This
work was supported in part by the US Air Force Grant F49620-03-1-0387. |
||||||||||||||
last update on February 1st, 2008 |