Spatial Resolution Enhancement of Oversampled Images Using Regression Decomposition and Synthesis

TL;DR

This paper introduces a novel regression-based statistical model that decomposes and synthesizes sub-regression models to enhance the spatial resolution of oversampled images, validated through simulation experiments.

Contribution

It proposes a new regression decomposition and synthesis approach that incorporates design matrix structure for improved spatial resolution enhancement.

Findings

Effective resolution enhancement demonstrated in simulated oversampled images

Model provides reliable estimation by leveraging sparse design matrix structure

Simulation results confirm feasibility and robustness of the approach

Abstract

A new statistical model designed for regression analysis with a sparse design matrix is proposed. This new model utilizes the positions of the limited non-zero elements in the design matrix to decompose the regression model into sub-regression models. Statistical inferences are further made on the values of these limited non-zero elements to provide a reference for synthesizing these sub-regression models. With this concept of the regression decomposition and synthesis, the information on the structure of the design matrix can be incorporated into the regression analysis to provide a more reliable estimation. The proposed model is then applied to resolve the spatial resolution enhancement problem for spatially oversampled images. To systematically evaluate the performance of the proposed model in enhancing the spatial resolution, the proposed approach is applied to the oversampled images that are reproduced via random field simulations. These application results based on different generated scenarios then conclude the effectiveness and the feasibility of the proposed approach in enhancing the spatial resolution of spatially oversampled images.