Blind Restoration of High-Resolution Ultrasound Video
Journal:
arXiv
Published Date:
May 20, 2025
Abstract
Ultrasound imaging is widely applied in clinical practice, yet ultrasound
videos often suffer from low signal-to-noise ratios (SNR) and limited
resolutions, posing challenges for diagnosis and analysis. Variations in
equipment and acquisition settings can further exacerbate differences in data
distribution and noise levels, reducing the generalizability of pre-trained
models. This work presents a self-supervised ultrasound video super-resolution
algorithm called Deep Ultrasound Prior (DUP). DUP employs a video-adaptive
optimization process of a neural network that enhances the resolution of given
ultrasound videos without requiring paired training data while simultaneously
removing noise. Quantitative and visual evaluations demonstrate that DUP
outperforms existing super-resolution algorithms, leading to substantial
improvements for downstream applications.
