Analog Image Modeling for 3D Single Image Super Resolution and Pansharpening

Frontiers in Applied Mathematics and Statistics

Claas Grohnfeldt¹  Richard Lartey²  Weihong Guo²  Xiaoxiang Zhu³ 

[1] Department of Aerospace and Geodesy, Technical University of Munich, Munich, Germany;Department of Mathematics, Applied Mathematics and Statistics, Case Western Reserve University, Cleveland, OH, United States;German Aerospace Center (DLR), Wessling, Germany;

关键词: super-resolution;    reproducible kernel Hilbert space (RKHS);    heaviside;    sparse representation;    multispectral imaging;   

DOI  :  10.3389/fams.2020.00022

来源: DOAJ

【 摘 要 】

Image super-resolution is an image reconstruction technique which attempts to reconstruct a high resolution image from one or more under-sampled low-resolution images of the same scene. High resolution images aid in analysis and inference in a multitude of digital imaging applications. However, due to limited accessibility to high-resolution imaging systems, a need arises for alternative measures to obtain the desired results. We propose a three-dimensional single image model to improve image resolution by estimating the analog image intensity function. In recent literature, it has been shown that image patches can be represented by a linear combination of appropriately chosen basis functions. We assume that the underlying analog image consists of smooth and edge components that can be approximated using a reproducible kernel Hilbert space function and the Heaviside function, respectively. We also extend the proposed method to pansharpening, a technology to fuse a high resolution panchromatic image with a low resolution multi-spectral image for a high resolution multi-spectral image. Various numerical results of the proposed formulation indicate competitive performance when compared to some state-of-the-art algorithms.

【 授权许可】

Unknown