My Account | Careers | Downloads | Blog

PROJECT TITLE :

A Variational Pansharpening Approach Based on Reproducible Kernel Hilbert Space and Heaviside Function - 2018

ABSTRACT:

Pansharpening is a vital application in remote sensing Image Processing. It will increase the spatial-resolution of a multispectral image by fusing it with a high spatial-resolution panchromatic image in the identical scene, that brings great favor for subsequent processing like recognition, detection, etc. During this Project, we propose endless modeling and sparse optimization based mostly method for the fusion of a panchromatic image and a multispectral image. The proposed model is mainly primarily based on reproducing kernel Hilbert space (RKHS) and approximated Heaviside function (AHF). Further, we additionally propose a Toeplitz sparse term for representing the correlation of adjacent bands. The model is convex and solved by the alternating direction methodology of multipliers that guarantees the convergence of the proposed method. Intensive experiments on many real datasets collected by totally different sensors demonstrate the effectiveness of the proposed technique as compared with many state-of-the-art pansharpening approaches.


Did you like this research project?

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

PROJECT TITLE : Medical data wrangling with sequential variational autoencoders ABSTRACT: The majority of the time, medical data sets are flawed due to noise and gaps in the data. It is a common misconception that these missing
PROJECT TITLE : Robust Variational Learning for Multiclass Kernel Models With Stein Refinement ABSTRACT: The ability of kernel-based models to generalize well is impressive, but the vast majority of them, including the SVM, are
PROJECT TITLE : Discriminative Mixture Variational Autoencoder for Semisupervised Classification ABSTRACT: For the purpose of feature extraction in semisupervised learning, a deep probability model known as the discriminative
PROJECT TITLE : A Novel Retinex-Based Fractional-Order Variational Model for Images With Severely Low Light ABSTRACT: A new fractional-order variational model based on Retinex is proposed in this research for low-light photos.
PROJECT TITLE : Variational Bayesian Blind Color Deconvolution of Histopathological Images ABSTRACT: In most whole-slide histology images, two or more chemical dyes are used. In digital pathology, slide stain separation or colour

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

Project Enquiry