Nonlocal Patch Tensor Sparse Representation for Hyperspectral Image Super-Resolution


Super-resolution of hyperspectral images (HSI) is shown in this study, which combines low-resolution (LR) HSI with high-resolution multispectral images (HR) to produce high-resolution HSI (HR-HSI). The nonlocal patch tensor is initially extracted from the nonlocal similar patches in order to build the proposed technique (NPT). To model the extracted NPTs, a new tensor sparse representation based on tensor-tensor product (t - product) is presented. To preserve both the spectral and spatial similarity of nonlocally identical patches, we use tensor sparse representation. In order to create a single objective function that incorporates nonlocal similarity, lexical learning, and sparse coding, the relationship between the HR-HSI and the LR-HSI is established using t - product. In the end, the optimization problem is solved using the alternating direction approach of multipliers. Three data sets and a real data set show that the suggested method significantly outperforms the current state-of-the art HSI super-resolution methods.

Did you like this research project?

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

PROJECT TITLE : A Data Dependent Multiscale Model for Hyperspectral Unmixing With Spectral Variability ABSTRACT: Environmental, lighting, atmospheric, and temporal variables can all contribute to hyperspectral image spectral
PROJECT TITLE : Hyperspectral Imagery Classification via Stochastic HHSVMs ABSTRACT: The use of hyperspectral imagery (HSI) in real-world applications has demonstrated encouraging outcomes. Two key obstacles in HSI classification
PROJECT TITLE : Nonconvex-Sparsity and Nonlocal-Smoothness-Based Blind Hyperspectral Unmixing ABSTRACT: When it comes to hyperspectral data, blind unmixing (HU) is an essential technique for decomposing mixed images into constituent
PROJECT TITLE :A MAP-Based Approach for Hyperspectral Imagery Super-Resolution - 2018ABSTRACT:In this Project, we have a tendency to propose a novel single image Bayesian super-resolution (SR) algorithm where the hyperspectral
PROJECT TITLE : Tensor decomposition and PCA jointed algorithm For hyperspectral image denoising - 2016 ABSTRACT: Denoising is a important preprocessing step for hyperspectral image (HSI) classification and detection. Ancient

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

Project Enquiry