Blind Restoration of High-Resolution Ultrasound Video
This work addresses challenges in clinical ultrasound imaging by improving video quality for better diagnosis, though it appears incremental as it builds on existing super-resolution methods with a self-supervised adaptation.
The paper tackles the problem of low signal-to-noise ratios and limited resolutions in ultrasound videos, which hinder diagnosis, by introducing a self-supervised super-resolution algorithm called Deep Ultrasound Prior (DUP) that enhances resolution and removes noise without paired training data, resulting in outperformance of existing algorithms and substantial improvements for downstream applications.
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.