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Image Restoration Based on Adaptive Dual-Domain Filtering

机译：基于自适应双域滤波的图像恢复

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摘要

Image restoration is a long-standing problem in low-level computer vision. In this paper, we offer a simple but effective estimation paradigm for various image restoration problems. Specifically, we first propose a model-based Gaussian denoising method Adaptive Dual-Domain Filtering (ADDF) by learning the optimal confidence factors which are adjusted adaptively with Gaussian noise standard deviation. In addition, by generalizing this learning approach to Laplace noise, the learning algorithm of the optimum confidence factors in Laplace denoising is presented. Finally, the proposed ADDF is tactfully plugged into the method frameworks of off-the-shelf image deblurring and single image super-resolution (SISR). The approach, coining the name Plug-ADDF, achieves promising performance. Extensive experiments validate that the proposed ADDF for Gaussian and Laplace noise removals indeed results in visual and quantitative improvements over some existing state-of-the-art methods. Moreover, our Plug-ADDF for image deblurring and SISR also demonstrates superior performance objectively and subjectively.
机译：在低级计算机视觉中，图像恢复是一个长期存在的问题。在本文中，我们为各种图像恢复问题提供了一种简单而有效的估计范例。具体来说，我们首先通过学习最佳置信因子（基于高斯噪声标准偏差进行自适应调整），提出一种基于模型的高斯去噪方法自适应双域滤波（ADDF）。此外，通过将这种学习方法推广到拉普拉斯噪声，提出了拉普拉斯去噪中最佳置信因子的学习算法。最后，将拟议的ADDF巧妙地插入到现有图像去模糊和单图像超分辨率（SISR）的方法框架中。该方法的名称为Plug-ADDF，实现了令人鼓舞的性能。大量的实验证明，所提出的用于去除高斯和拉普拉斯噪声的ADDF确实在视觉上和数量上比现有的一些最新方法有所改进。此外，我们的用于图像去模糊和SISR的Plug-ADDF也在客观和主观上展示出卓越的性能。

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