A largest matching area approach to image denoising - 2016


Given the success of patch-based approaches to image denoising, this paper addresses the unwell-posed problem of patch size selection. Massive patch sizes improve noise robustness in the presence of excellent matches, however can also lead to artefacts in textured regions due to the rare patch impact; smaller patch sizes reconstruct details a lot of accurately but risk over-fitting to the noise in uniform regions. We have a tendency to propose to jointly optimize each matching patch's identity and size for grayscale image denoising, and present several implementations. The new approach effectively selects the most important matching areas, subject to the constraints of the out there knowledge and noise level, to enhance noise robustness. Experiments on customary take a look at pictures demonstrate our approach's ability to enhance on fixed-size reconstruction, particularly at high noise levels, on smoother image regions.

Did you like this research project?

To get this research project Guidelines, Training and Code... Click Here

PROJECT TITLE : Hop-by-Hop Message Authenticationand Source Privacy in WirelessSensor Networks - 2014 ABSTRACT: Message authentication is one of the most effective ways to thwart unauthorized and corrupted messages from being
PROJECT TITLE : Fast and Secure Multihop Broadcast Solutions for Intervehicular Communication - 2014 ABSTRACT: Intervehicular communication (IVC) is an important emerging research area that is expected to considerably contribute
PROJECT TITLE : Cross-Layer Approach for Minimizing Routing Disruption in IP Networks - 2014 ABSTRACT: Backup paths are widely used in IP networks to protect IP links from failures. However, existing solutions such as the commonly
PROJECT TITLE :Quality-Differentiated Video Multicast in Multirate Wireless Networks - 2013ABSTRACT:Adaptation of modulation and transmission bit-rates for video multicast in a multirate wireless network is a challenging problem
PROJECT TITLE :Network Traffic Classification Using Correlation Information - 2013ABSTRACT:Traffic classification has wide applications in network management, from security monitoring to quality of service measurements. Recent

Ready to Complete Your Academic MTech Project Work In Affordable Price ?

Project Enquiry