MSAD-Net Enables High-Fidelity Denoising of Low-Power 3PM Images of Muscle Stem Cells (MuSCs) (IMAGE)
Caption
a Overview of the Multi-Scale Attention Denoising Network (MSAD-Net) to recover images of high SNR from those of low SNR. b-d Typical noisy images and corresponding denoised images of (b) vascular endothelial cells (ECs), (c) MuSCs, and (d) macrophages (Mφs) (scale bar: 100 μm). Top-left panels showed the noisy images. Bottom-left panels showed the images denoised by the MSAD-Net. Top-right panels showed insets ‘(a)’ and ‘(b)’ in noisy and denoised images (scale bar: 20 μm). Bottom-right panels showed intensity along dotted lines in noisy and denoised images in top-right panels. e The original noisy image and corresponding denoised output images of MuSCs by various methods, including Gaussian filter, Median filter, BM3D, Pix2Pix, DNCNN, Masked Denoising, UNet and MSAD-Net. The image with high SNR was set as ground truth for comparison. Imaging depth: 150 μm. Imaging condition: 6 mW excitation power and 3 μs/pixel scanning time for noisy imaging while 15 mW excitation power and 12 μs/pixel scanning time (common conditions) for ground truth imaging. Scale bar: 100 μm. f Intensity profiles along the white dashed lines across MuSCs in (e). Insert was the enlarged intensity profiles in rectangular box. Three deep learning networks (Masked Denoising, UNet and MSAD-Net) with relatively good denoising effects were selected for comparison.
Credit
Yifei Li et al., Zhejiang University, PhotoniX 2025
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License
CC BY