论文标题
选择基于卷积神经网络去膨胀的好区域
Select Good Regions for Deblurring based on Convolutional Neural Networks
论文作者
论文摘要
盲图片脱毛的目的是用一个未知的模糊内核从一个输入模糊图像中恢复尖锐的图像。但是，大多数图像脱毛方法都集中在开发图像先验上，但是，对图像细节和结构对模糊内核估计的影响没有足够的关注。什么是有用的图像结构以及如何选择一个良好的脱蓝色区域？在这项工作中，我们提出了一种深层神经网络模型方法，用于选择良好的区域以估计内核。首先，我们使用标签构建图像贴片并训练深层神经网络，然后应用学习的模型来确定图像的哪个区域最适合DeBlur。实验结果表明，所提出的方法是有效的，并且可以为图像去膨胀选择良好的区域。
The goal of blind image deblurring is to recover sharp image from one input blurred image with an unknown blur kernel. Most of image deblurring approaches focus on developing image priors, however, there is not enough attention to the influence of image details and structures on the blur kernel estimation. What is the useful image structure and how to choose a good deblurring region? In this work, we propose a deep neural network model method for selecting good regions to estimate blur kernel. First we construct image patches with labels and train a deep neural networks, then the learned model is applied to determine which region of the image is most suitable to deblur. Experimental results illustrate that the proposed approach is effective, and could be able to select good regions for image deblurring.
