发表机构
Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences; University of Chinese Academy of Sciences; University of Melbourne; Chinese Academy of Sciences（中国科学院长春光学精密机械与物理研究所; 中国科学院大学; 墨尔本大学; 中国科学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
PP-Net提出混合物理先验神经网络，通过DFN-Net、ASAP和GF-Net三组件去除生物医学图像散射光，在合成基准上PSNR提升超10.8 dB，NIQE降低43.3%，并实现嵌入式设备高效部署。
AI 中文摘要
散射光在生物医学图像中普遍存在，但其去除仍然具有挑战性。困难源于三个方面：首先，对齐的无散射光生物医学真值通常不可用；其次，散射与弱照明和传感器引起的噪声耦合；第三，许多基于学习的恢复模型在医疗物联网（IoMT）场景中的嵌入式设备上计算成本高昂。为解决这些问题，本文提出PP-Net，一种用于生物医学散射光去除的混合物理先验神经网络。所提方法由三个组件组成：DFN-Net抑制传感器引起的噪声，ASAP估计散射图并恢复基于物理的先验图，GF-Net通过将先验图与去噪观测融合来细化先验图。为减少对成对生物医学真值的依赖，开发了一种渐进式合成训练和跨域迁移策略。实验表明，物理先验分支在成对合成基准上将峰值信噪比（PSNR）提高了最多1.26 dB。在联合噪声和散射退化下，与代表性基线方法相比，PP-Net将PSNR提高了超过10.8 dB，将结构相似性指数（SSIM）提高了超过0.62。在真实W2S生物医学图像上，所提方法将平均自然图像质量评估器（NIQE）分数降低了43.3%。通过RKNN转换和INT8量化的边缘部署，在360张测试图像上实现了每张512×512图像约200毫秒的平均推理延迟。这些结果表明，PP-Net为IoMT场景中的显微成像、内窥镜检查以及边缘辅助生物医学分析提供了有效且可部署的解决方案。
英文摘要
Scattered light is common in biomedical images, yet its removal remains challenging. The difficulty arises from three aspects: first, aligned scattered-light-free biomedical ground truth is often unavailable; second, scattering is coupled with weak illumination and sensor-induced noise; and third, many learning-based restoration models are computationally expensive for embedded devices in Internet of Medical Things (IoMT) scenarios. To address these issues, this paper proposes PP-Net, a hybrid physical-prior neural network for biomedical scattered light removal. The proposed method consists of three components: DFN-Net suppresses sensor-induced noise, ASAP estimates the scattering map and recovers a physics-based prior map, and GF-Net refines the prior map by fusing it with the denoised observation. To reduce the dependence on paired biomedical ground truth, a progressive synthetic training and cross-domain transfer strategy is developed. Experiments show that the physical-prior branch improves the peak signal-to-noise ratio (PSNR) by up to 1.26 dB on paired synthetic benchmarks. Under joint noise-and-scattering degradation, PP-Net improves PSNR by more than 10.8 dB and the structural similarity index measure (SSIM) by more than 0.62 compared with representative baseline methods. On real W2S biomedical images, the proposed method reduces the average Natural Image Quality Evaluator (NIQE) score by 43.3\%. Edge deployment with RKNN conversion and INT8 quantization achieves an average inference latency of approximately 200 ms per $512\times512$ image over 360 test images. These results demonstrate that PP-Net provides an effective and deployable solution for microscopic imaging, endoscopic inspection, and edge-assisted biomedical analysis in IoMT scenarios.
