PROJECT TITLE :

NLH A Blind Pixel-Level Non-Local Method for Real World Image Denoising

ABSTRACT:

Using non-local self similarity (NSS) as an image denoising prior is powerful. Patch-level NSS priors are used in most existing denoising algorithms. A pixel-level NSS prior is introduced in this research, which means searching for similar pixels in a non-local area. This is due to the fact that it is easier to discover roughly similar pixels in artificial images than it is to find similar patches in natural photos. NSS priors allow us to construct an accurate noise level estimate approach and then a blind picture denoising method using the lifting Haar transform and Wiener filtering techniques, respectively. Proposed methods perform better on benchmark datasets than earlier non-deep methods and are still competitive with current state of the art deep learning based methods for denoising images from the real world.


Did you like this research project?

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


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
PROJECT TITLE :Distance Bounding A Practical Security Solution for Real-Time Location Systems - 2013ABSTRACT:The need for implementing adequate security services in industrial applications is increasing. Verifying the physical
PROJECT TITLE :T-Drive Enhancing Driving Directions with Taxi Drivers’ Intelligence - 2013ABSTRACT:This paper presents a smart driving direction system leveraging the intelligence of experienced drivers. In this system, GPS-equipped
PROJECT TITLE :Distributed Web Systems Performance Forecasting Using Turning Bands Method - 2013ABSTRACT:With the increasing development of distributed computer systems (DCSs) in networked industrial and manufacturing applications
PROJECT TITLE :A Fast Clustering-Based Feature Subset Selection Algorithm for High-Dimensional Data - 2013ABSTRACT:Feature selection involves identifying a subset of the most useful features that produces compatible results as

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

Project Enquiry