Select Good Regions for Deblurring based on Convolutional Neural Networks

TL;DR

This paper introduces a deep neural network approach to identify optimal image regions for blur kernel estimation, improving blind image deblurring by focusing on structurally informative areas.

Contribution

It proposes a novel neural network-based method for selecting good regions for deblurring, addressing the lack of focus on image details in existing approaches.

Findings

Effective selection of regions improves deblurring quality

Neural network accurately identifies suitable regions for kernel estimation

Method outperforms traditional region selection techniques

Abstract

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.