Abstract
A new algorithm for solving the deconvolution problem is proposed. This algorithm uses the wavelet transform to induce a multiresolution approach to deconvolve a blurred signal/image. The low resolution part of a signal/ image is restored first and then high resolution information is added successively into the estimation process. Two 'different ways to incorporate the image space positivity constraint, namely loosely and strictly, are discussed. In contrast to most restoration algorithms, the positivity constraint is applied directly in the transformed domain. The performance of the algorithm in the presence of noise is also investigated. © lEE, 1997.
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 callMore From: IEE Proceedings - Vision, Image, and Signal Processing
Disclaimer: 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.
