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.

翻译：超声（US）图像判读受到乘性斑点噪声、点扩散函数（PSF）引起的采集模糊以及依赖于扫描设备和操作人员的伪影的阻碍。有监督增强方法通常假设存在干净的目标图像或已知的退化过程，而这些条件在实践中很少满足。本文提出了一种盲自监督增强框架，该框架使用Swin卷积U-Net，通过一个**物理引导**的退化模型进行训练，以联合对B模式图像进行去卷积和去噪。对于每个训练帧，我们提取旋转/裁剪后的图像块，并通过以下方式合成输入图像：（i）与高斯PSF代理进行卷积；（ii）通过空间加性高斯噪声或模拟相位/幅度畸变的复傅里叶域扰动来注入噪声。对于超声扫描，通过非局部低秩（NLLR）去噪获得类干净目标图像，从而无需真实标签；对于自然图像，原始图像即作为目标。该方法在UDIAT~B、JNU-IFM和XPIE Set-P数据集上进行训练和验证，并在包含700幅图像的PSFHS测试集上进行额外评估。结果表明，该方法在高斯噪声和斑点噪声水平下均取得了最高的PSNR/SSIM值，且在更强的退化条件下优势更为明显。相较于MSANN、Restormer和DnCNN，在严重高斯噪声下，该方法通常能额外保持约1–4 dB的PSNR和0.05–0.15的SSIM；在严重斑点噪声下，能额外保持约2–5 dB的PSNR和0.05–0.20的SSIM。受控PSF研究显示其具有更小的半高全宽（FWHM）和更高的峰值梯度，证明了其在不损失边缘锐度的情况下恢复分辨率的能力。作为即插即用的预处理模块，该方法能持续提升胎儿头部和耻骨联合分割的Dice系数。总体而言，该方法为鲁棒的超声图像增强提供了一条实用且假设条件宽松的路径，能够泛化至不同的数据集、扫描设备和退化类型。