A Novel Fast 3D Single Image Super-Resolution Algorithm
This work addresses the computational bottleneck in 3D super-resolution for applications like medical imaging, though it appears incremental as it builds on existing regularization techniques.
The paper tackles the 3D single image super-resolution problem by developing a computationally efficient method that reduces the computational cost of state-of-the-art algorithms, with numerical experiments showing it outperforms existing 3D SR methods.
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.