Name
Abstract | ||
|---|---|---|
We propose an unbiased estimate of a filtered version of the mean squared error - the blur-SURE (Stein's unbiased risk estimate)-as a novel criterion for estimating an unknown point spread function (PSF) from the degraded image only. The PSF is obtained by minimizing this new objective functional over a family of Wiener processings. Based on this estimated blur kernel, we then perform nonblind deconvolution using our recently developed algorithm. The SURE-based framework is exemplified with a number of parametric PSF, involving a scaling factor that controls the blur size. A typical example of such parametrization is the Gaussian kernel. The experimental results demonstrate that minimizing the blur-SURE yields highly accurate estimates of the PSF parameters, which also result in a restoration quality that is very similar to the one obtained with the exact PSF, when plugged into our recent multi-Wiener SURE-LET deconvolution algorithm. The highly competitive results obtained outline the great potential of developing more powerful blind deconvolution algorithms based on SURE-like estimates. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1109/TIP.2014.2380174 | IEEE Transactions on Image Processing |
Keywords | Field | DocType |
noise,image restoration,kernel,estimation,minimization,deconvolution | Blind deconvolution,Deconvolution,Artificial intelligence,Image restoration,Point spread function,Gaussian function,Kernel (linear algebra),Wiener filter,Mathematical optimization,Pattern recognition,Algorithm,Parametric statistics,Mathematics | Journal |
Volume | Issue | ISSN |
24 | 2 | 1057-7149 |
Citations | PageRank | References |
11 | 0.56 | 36 |
Authors | ||
2 | ||