使用去模糊模糊内核对湍流图像进行去模糊处理

Lizhen Duan, Libo Zhong, Jianlin Zhang
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引用次数: 0

摘要

在处理有噪声的湍流降解图像时,通常先使用去噪低通滤波器,然后再实施去模糊算法。然而,这种滤波器不仅会抑制噪声,还会对降解图像造成一定程度的模糊。这种模糊效应会造成对真实模糊内核的估计模糊,最终导致对潜在清晰图像的估计失真。为解决这一问题,本文提出了一种创新的单图像去模糊方法。它集成了一个专门的模糊核去模糊步骤,以减轻去噪滤波器的影响。L0 准则和 L2 准则分别作为潜在清晰图像和模糊核的约束条件。在合成和真实世界湍流降解图像上的实验结果证明了所提方法的有效性和效率。
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Turbulent image deblurring using a deblurred blur kernel
In the context of addressing a noisy turbulence-degraded image, it is common to use a denoising low-pass filter before implementing a deblurring algorithm. However, this filter not only suppresses noise but also induces a certain degree of blur into the degraded image. This blur effect causes a blurred estimate of the true blur kernel and ultimately leads to a distorted estimate of the latent clear image. To tackle this issue, this paper presents an innovative single-image deblurring method. It integrates a dedicated blur kernel deblurring step to mitigate the effects of the denoising filter. The L0 norm and L2 norm serve as the respective constraints for latent clear image and blur kernel. Experimental results on both synthetic and real-world turbulence-degraded images demonstrate the effectiveness and efficiency of the proposed method.
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