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

• Main Authors: Richard Lartey, Weihong Guo, Xiaoxiang Zhu, Claas Grohnfeldt
• Format: Article
• Language: English
• Published: Frontiers Media S.A. 2020-06-01
• Series: Frontiers in Applied Mathematics and Statistics
• Subjects: super-resolution; reproducible kernel Hilbert space (RKHS); heaviside; sparse representation; multispectral imaging
• Online Access: https://www.frontiersin.org/article/10.3389/fams.2020.00022/full

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.