Li Taihao, Chen Huai, Zhang Min, Liu Shupeng, Xia Shunren, Cao Xinhua, Young Geoffrey S, Xu Xiaoyin
College of Medical Instruments, Shanghai University of Medicine and Health Sciences, Shanghai, China.
Beijing Advanced Innovation Center for Imaging Technology, Capital Normal University, Beijing, China.
Pattern Recognit. 2019 Jun;90:134-146. doi: 10.1016/j.patcog.2019.01.019. Epub 2019 Jan 17.
In many applications, image deblurring is a pre-requisite to improve the sharpness of an image before it can be further processed. Iterative methods are widely used for deblurring images but care must be taken to ensure that the iterative process is robust, meaning that the process does not diverge and reaches the solution reasonably fast, two goals that sometimes compete against each other. In practice, it remains challenging to choose parameters for the iterative process to be robust. We propose a new approach consisting of relaxed initialization and pixel-wise updates of the step size for iterative methods to achieve robustness. The first novel design of the approach is to modify the initialization of existing iterative methods to stop a noise term from being propagated throughout the iterative process. The second novel design is the introduction of a vectorized step size that is adaptively determined through the iteration to achieve higher stability and accuracy in the whole iterative process. The vectorized step size aims to update each pixel of an image individually, instead of updating all the pixels by the same factor. In this work, we implemented the above designs based on the Landweber method to test and demonstrate the new approach. Test results showed that the new approach can deblur images from noisy observations and achieve a low mean squared error with a more robust performance.
在许多应用中，图像去模糊是在对图像进行进一步处理之前提高其清晰度的一个先决条件。迭代方法被广泛用于图像去模糊，但必须注意确保迭代过程的稳健性，这意味着该过程不会发散并且能相当快速地得到解决方案，而这两个目标有时会相互矛盾。在实践中，为使迭代过程稳健而选择参数仍然具有挑战性。我们提出了一种新方法，该方法由迭代方法的松弛初始化和步长的逐像素更新组成，以实现稳健性。该方法的第一个新颖设计是修改现有迭代方法的初始化，以阻止噪声项在整个迭代过程中传播。第二个新颖设计是引入一个矢量化步长，该步长通过迭代自适应确定，以在整个迭代过程中实现更高的稳定性和准确性。矢量化步长旨在分别更新图像的每个像素，而不是以相同的因子更新所有像素。在这项工作中，我们基于兰德韦伯方法实现了上述设计，以测试和演示这种新方法。测试结果表明，新方法能够从有噪声的观测中对图像进行去模糊，并以更稳健的性能实现较低的均方误差。
