Blind Ultrasound Image Enhancement via Self-Supervised Physics-Guided Degradation Modeling

• URL: http://arxiv.org/abs/2601.21856v1
• Date: Thu, 29 Jan 2026 15:28:25 GMT
• Title: Blind Ultrasound Image Enhancement via Self-Supervised Physics-Guided Degradation Modeling
• Authors: Shujaat Khan, Syed Muhammad Atif, Jaeyoung Huh, Syed Saad Azhar,
• Abstract summary: Supervised enhancement methods assume access to clean targets or known degradations.<n>We present a blind, self-supervised enhancement framework that jointly deconvolves and denoises B-mode images.
• Score: 4.619828919345113
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Ultrasound (US) interpretation is hampered by multiplicative speckle, acquisition blur from the point-spread function (PSF), and scanner- and operator-dependent artifacts. Supervised enhancement methods assume access to clean targets or known degradations; conditions rarely met in practice. We present a blind, self-supervised enhancement framework that jointly deconvolves and denoises B-mode images using a Swin Convolutional U-Net trained with a \emph{physics-guided} degradation model. From each training frame, we extract rotated/cropped patches and synthesize inputs by (i) convolving with a Gaussian PSF surrogate and (ii) injecting noise via either spatial additive Gaussian noise or complex Fourier-domain perturbations that emulate phase/magnitude distortions. For US scans, clean-like targets are obtained via non-local low-rank (NLLR) denoising, removing the need for ground truth; for natural images, the originals serve as targets. Trained and validated on UDIAT~B, JNU-IFM, and XPIE Set-P, and evaluated additionally on a 700-image PSFHS test set, the method achieves the highest PSNR/SSIM across Gaussian and speckle noise levels, with margins that widen under stronger corruption. Relative to MSANN, Restormer, and DnCNN, it typically preserves an extra $\sim$1--4\,dB PSNR and 0.05--0.15 SSIM in heavy Gaussian noise, and $\sim$2--5\,dB PSNR and 0.05--0.20 SSIM under severe speckle. Controlled PSF studies show reduced FWHM and higher peak gradients, evidence of resolution recovery without edge erosion. Used as a plug-and-play preprocessor, it consistently boosts Dice for fetal head and pubic symphysis segmentation. Overall, the approach offers a practical, assumption-light path to robust US enhancement that generalizes across datasets, scanners, and degradation types.

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