Name
Abstract | ||
|---|---|---|
Motion blur in photographic images is a result of camera movement or shake. Methods such as Blind Deconvolution are used when information about the direction and size of blur is not known. Restoration methods, such as Lucy and Richardson or Wiener reconstruction use information about the direction and size of the blur in the deconvolution kernel (called Point Spread Function - PSF). Correct and fast determination of the direction and size of blur improves the quality of restoration and it can substantially reduce the computational time. In this article, a fast method for finding the direction and size of the blur automatically is presented. The method is based on computation of the power spectrum of the image gradient in the frequency domain. The method has achieved good results on both types of images: artificially blurred and naturally blurred (by the camera shake). |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1016/j.dsp.2010.03.012 | Digital Signal Processing |
Keywords | Field | DocType |
camera shake,fast method,computational time,motion blur,fast fourier transform,restoration method,point spread function,blurred image restoration,wiener reconstruction use information,camera movement,fast determination,image restoration,motion length,blind deconvolution,deconvolution,power spectrum,frequency domain | Computer vision,Richardson–Lucy deconvolution,Image gradient,Blind deconvolution,Pattern recognition,Deconvolution,Motion blur,Fast Fourier transform,Artificial intelligence,Image restoration,Point spread function,Mathematics | Journal |
Volume | Issue | ISSN |
20 | 6 | Digital Signal Processing |
Citations | PageRank | References |
17 | 1.09 | 13 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Michal Dobeš | 1 | 17 | 1.43 |
| Libor Machala | 2 | 17 | 1.43 |
| Tomáš Fürst | 3 | 17 | 1.09 |