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OPERA net Otsu驱动的性能增强的图像恢复算法

Pallvi Sharma¹, Priyanka Jarial², Bhim Sain Singla³

¹Department of Computer Science and Engineering, Punjabi University Patiala, Patiala, Punjab, 47002, India. pallvigautam5618@gmail.com.

Scientific reports

|December 9, 2025

概括

这项研究介绍了一种混合图像消除技术,该技术结合了波形变换和非局部介质过. 该方法有效地消除噪音,同时保留图像细节,优于现有的方法.

科学领域:

数字图像处理数字图像处理
计算机视觉计算机视觉

背景情况:

现实世界的数字图像遭受各种类型的噪音 (例如,高斯式,波桑式,盐和胡式).
现有的图像消除技术经常面临计算复杂性和过度平滑的挑战,导致重要的图像特征丢失.
有效的降噪对于保持图像质量和结构完整性至关重要.

研究的目的:

为了引入一种新的混合图像消除技术.
解决现有方法的局限性,特别是计算复杂性和过度平滑.
为了提高边缘保护和整体图像质量在denoising过程中.

主要方法:

建议采用混合方法,将波形变换和非局部平均值 (NLM) 过结合起来.
增强的Otsu值被整合到波形变换阶段中,用于初始降低噪音.
随后应用NLM过,以进一步细化无色化图像并保留边缘.

主要成果:

拟议的混合技术在柯达24数据集上实现了卓越的性能.
量化指标显示了显著的改善:PSNR (34.86),SSIM (0.93) 和RMSE (4.61).
使用FOM (0.99) 和VIF (0.59) 评分进行的进一步评估证实了该方法的有效性和优越性.

结论:

混合波形-NLM无声化方法为数字图像的噪声降低提供了有效的解决方案.
该技术成功地平衡了消除噪音的方法,同时保留了关键的图像结构和边缘.
实验结果验证了与传统方法相比,提议的方法的增强性能和稳定性.

关键词:

图像无效化图像无效化非本地平均值的非本地平均值.在Otsu上设有值.软门持有软门持有波段变换的波段变换是什么

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