IEEE Trans Image Process. 2018 Feb;27(2):580-593. doi: 10.1109/TIP.2017.2764625.
In this paper, we present a simple yet effective image deblurring method to produce ringing-free deblurred images. Our work is inspired by the observation that large-scale deblurring ringing artifacts are measurable through a multi-resolution pyramid of low-pass filtering of the blurred-deblurred image pair. We propose to model such a quantification as a convex cost function and minimize it directly in the deblurring process in order to reduce ringing regardless of its cause. An efficient primal-dual algorithm is proposed as a solution to this optimization problem. Since the regularization is more biased toward ringing patterns, the details of the reconstructed image are prevented from over-smoothing. An inevitable source of ringing is sensor saturation which can be detected costlessly contrary to most other sources of ringing. However, dealing with the saturation effect in deblurring introduces a non-linear operator in optimization problem. In this paper, we also introduce a linear approximation as a solution to handling saturation in the proposed deblurring method. As a result of these steps, we significantly enhance the quality of the deblurred images. Experimental results and quantitative evaluations demonstrate that the proposed method performs favorably against state-of-the-art image deblurring methods.
在本文中，我们提出了一种简单而有效的图像去模糊方法，以生成无振铃的去模糊图像。我们的工作受到以下观察结果的启发：通过对模糊-去模糊图像对进行多分辨率低通滤波的金字塔，可以测量到大尺度去模糊振铃伪像。我们建议将这种量化建模为凸代价函数，并在去模糊过程中直接最小化它，以减少振铃，而不管其原因如何。提出了一种有效的原对偶算法作为该优化问题的解决方案。由于正则化更偏向于振铃模式，因此防止重建图像的细节过度平滑。振铃的一个不可避免的来源是传感器饱和，与大多数其他振铃源不同，它可以免费检测到。然而，在去模糊中处理饱和效应会在优化问题中引入一个非线性算子。在本文中，我们还引入了一种线性近似作为处理所提出的去模糊方法中饱和的解决方案。由于这些步骤，我们显著提高了去模糊图像的质量。实验结果和定量评估表明，所提出的方法在图像去模糊方法中表现良好。
