Abstract
This paper uses a Fractional Integral Mask algorithm to remove Poisson noise from medical images. Riemann-Liouville definition of fractional calculus is used to create Fractional integral masks in eight. directions. Two different methods of Mask Combining Technique, CT-1 and CT-2 are introduced for image de-noising. Performance of the algorithm is compared with that of Gaussian smoothing method of noise removal. Results depict that the algorithm with combining technique, CT-2 is better compared to CT-1. Experiments show that the mask size required directly depends on the fractional order. Mask size can be reduced for lower fractional orders thus ensuring the computation complexity reduction for lower orders. The operational range of fractional orders for CT-1 and CT-2 is also estimated. De-noising performance is measured based on visual perception and Peak Signal to Noise Ratio.
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.
