Mendeley
TY  _  JOUR
ID  -  13980223179357
TI  -  Grayscale Images Deblurring Using Robust Optimization Problem in Uncertainty Conditions in Blurring Model Parameters
JO  -  Nashriyyah -i Muhandisi -i Barq va Muhandisi -i Kampyutar -i Iran
JA  -  ES
LA  -  fa
SN  -  16823745
AU  -  Mohammadi Zeinab
AU  -  Daneshifar  Ebrahim
AU  -  Ebrahimi moghadam Abbas
AU  -  Khademi Morteza
AD  -  فردوسی مشهد
AD  -  بین الملی امام رضا(ع)
AD  -  دانشگاه فردوسی
AD  -  دانشگاه فردوسی مشهد
Y1  -  2021
PY  -  2021
VL  -  4
IS  -  
SP  -  318
EP  -  326
KW  -  Semi-blind de-blurringimage de-blurring filterrobust optimizationworst-case optimization
DO  -  
N2  -  Nowadays, one of the most important issues in the field of image processing is image de-blurring. De-blurring of an image can be achieved via two different approaches; blind de-blurring and non-blind de-blurring. In blind de-blurring, the kernel by which the blur has occurred is assumed unknown, while in non-blind de-blurring, this kernel is given. In blind de-blurring, the blurring kernel must be estimated in order to sharpen the corrupted image. This may increase the computational cost of the de-blurring process. Non-blind image de-blurring is an ill-posed problem with linear reverse issues. Therefore, we develop optimization problems in order to estimate the original sharp images. Usually, non-blind de-blurring methods assume that the blurring kernel is error-free, however, in practice our knowledge of the PSF is uncertain. Hence, in this paper, we use a semi-blind method for de-blurring the blurred image that is robust to this uncertainty. The proposed robust optimization model is followed by a filter for image de-blurring that can attain the solution with lowest possible error in the worst case scenarios, that is, the maximum uncertainty about the blurring kernel. Based on the simulation results, our proposed semi-blind model yields more than 4 dB PSNR improvements compared to conventional blind image de-blurring methods.
UR  - rimag.ir/en/Article/28463
L1  - rimag.ir/en/Article/Download/28463
ER  -  
