Name
Abstract | ||
|---|---|---|
Kernel regression is an effective tool for a variety of image processing tasks such as denoising and interpolation [1]. In this paper, we extend the use of kernel regression for deblurring applications. In some earlier examples in the literature, such nonparametric deblurring was suboptimally 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 that generalizes some of the most popular regularization techniques in the literature. Experimental results demonstrate the effectiveness of our method. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1109/TIP.2007.918028 | IEEE Transactions on Image Processing |
Keywords | Field | DocType |
earlier example,optimal solution,image processing task,kernel regression,deblurs image,adaptive kernel regression,nonparametric deblurring,novel image,effective tool,deblurring application,degradation,algorithms,computer simulation,noise reduction,image processing,nonlinear filter,kernel,denoising,image reconstruction,convolution,interpolation,regression analysis | Kernel (linear algebra),Computer vision,Deblurring,Pattern recognition,Interpolation,Image processing,Regularization (mathematics),Artificial intelligence,Image restoration,Kernel method,Kernel regression,Mathematics | Journal |
Volume | Issue | ISSN |
17 | 4 | 1057-7149 |
Citations | PageRank | References |
64 | 2.28 | 16 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hiroyuki Takeda | 1 | 226 | 8.63 |
| Sina Farsiu | 2 | 1723 | 77.49 |
| Peyman Milanfar | 3 | 700 | 52.20 |