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A Gradient-Projected Model for Image Denoising

delete2025-12-19
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OA
AI
Y
Yuming Wen
Y
Yu Liu
Z
Zhaozhi Liang
G
Guangjun Xu
C
Cong Lin
G
Guancheng Wang *
DOI：10.3390/s26010013delete
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摘要

摘要

En 中文
数字图像在采集过程中容易受到各种噪声的影响，这会扭曲结构信息并阻碍后续处理。本研究提出了AuroraNet，一种去噪框架，它扩展了DudeNet的双分支设计，并集成了梯度投影函数（GPF）优化器以增强训练稳定性并保留精细尺度图像特征。我们在两个真实世界的噪声图像数据集上评估了该模型，以检验其在不同噪声条件下的鲁棒性。AuroraNet在第一个数据集上达到平均PSNR 35.59 dB，在第二个数据集上达到38.40 dB，同时在后者的SSIM为0.9633。在所有基准测试中，AuroraNet始终比多个现有模型和基线DudeNet提供更高的重建质量。尽管R-REDNet在一个数据集上获得了最高的整体分数，但AuroraNet在参数量少得多的情况下达到了相当的性能，凸显了其效率和实用价值。这些结果表明，AuroraNet为真实世界图像去噪提供了一个平衡的解决方案，在保持计算经济性的同时具备强大的去噪能力。
Keyword:
image denoising
gradient-projected function (GPF)
optimization
deep learning
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Sensors 封面图
Sensors
IF:
3.5
论文数:
7.2W
被引数:
20.9W

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