A hybrid deep learning framework for real-time speckle reduction and image enhancement on portable ultrasound systems.

backgroundSpeckle patterns in ultrasound images often obscure anatomical details, leading to diagnostic uncertainty. Recently, various deep learning-based techniques have been introduced to effectively suppress speckle; however, their high computational costs pose challenges for low-resource devices, such as portable ultrasound systems. PURPOSE: To address this issue, we introduce Edge Speckle Reduction and Image Enhancement (EdgeSRIE), a lightweight hybrid deep learning framework for real-time speckle reduction and image enhancement in portable ultrasound imaging.

methodsThe proposed framework consists of an unsupervised despeckling branch and a self-supervised deblurring branch trained separately in sequence with AdamW (learning rate 1 × 10

resultsAcross the four representative cases, post-training-quantized EdgeSRIE achieved the largest mean improvement in CNR (64.9 ± 21.0%) and the smallest mean decrease in AGM (-16.6 ± 33.2%). SSNR (109.8 ± 45.8%) was in the OBNLM-led range, whereas SSIM remained slightly lower than OSRAD and OBNLM on its native [0, 1] scale. The deployed model contained 17.67K parameters and reached 64.10 frames/s on the target hardware. In the case-matched analysis (n = 8), EdgeSRIE showed Holm-corrected significant advantages over all six baselines in CNR, over five of six in SSNR, and over four of six in AGM. For SSIM, EdgeSRIE was significantly higher than the four deep learning baselines but lower than OSRAD and OBNLM. Across the statistically significant comparisons, the magnitude-based effect sizes were large (Hedges' g ≥ 0.8).

conclusionsThese results support the feasibility of EdgeSRIE as a compact, deployment-oriented framework that balances speckle suppression, structural preservation, and real-time execution for portable ultrasound imaging.