On this page, we present image restoration results of our own developed technique, compared with standard methods for image restoration. The method we propose is a hybrid method based on steerable pyramids for noise reduction and blur estimaion (our method doesn't require knowledge about the Point Spread Function or PSF) and the Richardson-Lucy (RL) deconvolution method for deblurring, which we will call SPERRiL (Steerable Pyramid-based Estimation and Regularization of Richardson-Lucy restoration). In SPERRiL, we can choose to apply regularization every iteration, or once every n iterations.
True synthetic testimage
![]() Blur and Poisson noise) |
![]() Unregularized RL |
![]() RL + slight postblur |
![]() SPERRiL, n = 1 |
![]() SPERRiL, n = 3 |
![]() SPERRiL, n = 8 |
![]() Raw confocal image |
![]() Unregularized RL |
![]() RL + slight postblur |
![]() SPERRiL, n = 1 |
![]() SPERRiL, n = 2 |
![]() SPERRiL, n = 3 |