论文标题
基于框架的局部最小先验
Poissonian Blurred Image Deconvolution by Framelet based Local Minimal Prior
论文作者
论文摘要
图像生产工具并不总是创建清晰的图像，有时会创建嘈杂和模糊的图像。在这些情况下，泊松噪声是在天文学中拍摄的医学图像和图像中出现的最著名的声音之一。模糊的图像与泊松噪声掩盖了在医学或天文学中非常重要的重要细节。因此，研究人员总是考虑研究和提高受这种噪声影响的图像质量。在本文中，在第一步中，基于Framelet Transform，引入了局部最小的先验，在下一步中，该工具与分数计算一起用于Poissonian图像模糊反volution。在下文中，该模型被推广到盲案。为了评估提出的模型的性能，已经研究了一些图像，例如真实图像。
Image production tools do not always create a clear image, noisy and blurry images are sometimes created. Among these cases, Poissonian noise is one of the most famous noises that appear in medical images and images taken in astronomy. Blurred image with Poissonian noise obscures important details that are of great importance in medicine or astronomy. Therefore, studying and increasing the quality of images that are affected by this type of noise is always considered by researchers. In this paper, in the first step, based on framelet transform, a local minimal prior is introduced, and in the next step, this tool together with fractional calculation is used for Poissonian blurred image deconvolution. In the following, the model is generalized to the blind case. To evaluate the performance of the presented model, several images such as real images have been investigated.
