Select Good Regions for Deblurring based on Convolutional Neural Networks
This addresses a specific issue in image processing for applications like photography or computer vision, but it is incremental as it builds on existing deblurring methods by focusing on region selection.
The paper tackles the problem of blind image deblurring by proposing a deep neural network method to select good regions for blur kernel estimation, with experimental results showing the approach is effective.
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.