Abstract
The linear inverse problem encountered in restoration of blurred noisy images is typically solved via Tikhonov minimization. The outcome (restored image) of such minimization is highly dependent on the choice of regularization parameter. In the absence of prior information about the noise levels in the blurred image, finding this regularization parameter in an automated way becomes extremely challenging. The available methods like Generalized Cross Validation (GCV) may not yield optimal results in all cases. A novel method that relies on minimal residual method for finding the regularization parameter automatically is proposed here and was systematically compared with the GCV method. It was shown that the proposed method performance is superior to the GCV method in providing high quality restored images in cases where the noise levels are high.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
