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arXiv Machine Learning By Yongfei Guo, Tingjin Chu, Mengzhuo Liu, Hongwei Lou, Yuanhao Gong

PP-Net: A Hybrid Physical-Prior Neural Network for Scattered Light Removal in Biomedical Images on Embedded Devices

Read the original on arXiv Machine Learning →

PP‑Net is a hybrid physical‑prior neural network designed to remove scattered light from biomedical images on embedded devices. It combines a denoising network (DFN‑Net), a scattering‑map estimator (ASAP), and a refinement network (GF‑Net) to fuse a physics‑based prior with denoised observations. The method uses progressive synthetic training and cross‑domain transfer to reduce reliance on paired ground truth, achieving significant PSNR, SSIM, and NIQE improvements over baselines while maintaining an inference latency of about 200 ms per 512×512 image on edge hardware.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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