A Novel Fast 3D Single Image Super-Resolution Algorithm

TL;DR

This paper presents a fast, efficient 3D single image super-resolution algorithm that leverages frequency domain properties to improve computational speed and performance over existing methods.

Contribution

The paper introduces a novel decomposition of the 3D decimation operator enabling efficient regularization and super-resolution reconstruction.

Findings

Outperforms existing 3D SR methods in accuracy

Reduces computational cost significantly

Effective with various regularization functions

Abstract

This paper introduces a novel computationally efficient method of solving the 3D single image super-resolution (SR) problem, i.e., reconstruction of a high-resolution volume from its low-resolution counterpart. The main contribution lies in the original way of handling simultaneously the associated decimation and blurring operators, based on their underlying properties in the frequency domain. In particular, the proposed decomposition technique of the 3D decimation operator allows a straightforward implementation for Tikhonov regularization, and can be further used to take into consideration other regularization functions such as the total variation, enabling the computational cost of state-of-the-art algorithms to be considerably decreased. Numerical experiments carried out showed that the proposed approach outperforms existing 3D SR methods.