用局部/非局部先验处理图像反卷积中的噪声

Hicham Badri, H. Yahia
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引用次数: 2

摘要

非盲反卷积是指从已知核的模糊图像中恢复出清晰的潜在图像。即使已知内核,由于问题的病态性,反卷积图像通常包含令人不快的伪影。利用自然稀疏先验已被证明可以减少振铃伪影,但处理噪声仍然有限。另一方面,非局部先验在图像去噪方面的效果最好。本文提出将局部先验和非局部先验相结合来处理噪声。我们表明,模糊增加了图像内的自相似性,从而使非局部先验成为去噪模糊图像的一个很好的选择。然而,去噪引入了非高斯异常值,应该很好地建模。实验表明,与一些常用的图像重建方法相比,我们的方法在视觉和经验上都能得到更好的图像重建效果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Handling noise in image deconvolution with local/non-local priors
Non-blind deconvolution consists in recovering a sharp latent image from a blurred image with a known kernel. Deconvolved images usually contain unpleasant artifacts due to the ill-posedness of the problem even when the kernel is known. Making use of natural sparse priors has shown to reduce ringing artifacts but handling noise remains limited. On the other hand, non-local priors have shown to give the best results in image denoising. We propose in this paper to combine both local and non-local priors to handle noise. We show that the blur increases the self-similarity within an image and thus makes non-local priors a good choice for denoising blurred images. However, denoising introduces outliers which are not Gaussian and should be well modeled. Experiments show that our method produces a better image reconstruction both visually and empirically compared to methods some popular methods.
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