DenOiS: Dual-Domain Denoising of Observation and Solution in Ultrasound Image Reconstruction
Medical imaging aims to recover underlying tissue properties, using inexact (simplified/linearized) imaging models and often from inaccurate and incomplete measurements. Analytical reconstruction methods rely on hand-crafted regularization, sensitive to noise assumptions and parameter tuning. Among deep learning alternatives, plug-and-play (PnP) approaches learn regularization while incorporating imaging physics during inference, outperforming purely data-driven methods. The performance of all these approaches, however, still strongly depends on measurement quality and imaging model accuracy. In this work, we propose DenOiS, a framework that denoises both input observations and resulting ...
| Authors | Can Deniz Bezek, Orcun Goksel |
|---|---|
| Primary category | eess.IV |
| Published | 2 April 2026 |
| Last updated at source | 2 April 2026 |
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