High-quality non-blind image deconvolution with adaptive regularization
Non-blind image deconvolution is a process that obtains a sharp latent image from a blurred image when a point spread function (PSF) is known. However, ringing and noise amplification are inevitable artifacts in image deconvolution since perfect PSF estimation is impossible. The conventional regularization to reduce these artifacts cannot preserve image details in the deconvolved image when PSF estimation error is large, so strong regularization is needed. We propose a non-blind image deconvolution method which preserves image details, while suppressing ringing and noise artifacts by controlling regularization strength according to local characteristics of the image. In addition, the proposed method is performed fast with fast Fourier transforms so that it can be a practical solution to image deblurring problems. From experimental results, we have verified that the proposed method restored the sharp latent image with significantly reduced artifacts and it was performed fast compared to other non-blind image deconvolution methods.
non-blind image deconvolution adaptive regularization noise amplification local characteristics image deblurring ringing artifacts fast deconvolution boundary artifacts reduction