Abstract
The denoising of a natural image corrupted by noise is a classical problem in signal processing. A new method for image denoising is discussed. The bivariate shrinkage function based on the dependency between wavelet coefficients is derived from Bayesian maximum a posterior (MAP) estimation theory and applied to image denoising. The performance of the method is compared with that of the conventional soft thresholding technique. Experimental results show the method is satisfying in noise suppression, preserving edges and details.
Sign-in/Register to access full text options 
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.
