基于深度编码–解码器的图像模糊核估计

在图像去模糊问题中,显著边缘结构对图像的模糊核估计具有重要的作用.本文提出一种基于深度编码-解码器的图像模糊核估计算法.首先,通过构建训练数据集对深度编码-解码器进行训练,进而自适应地获得模糊图像的显著边缘结构;接着,结合显著边缘结构和模糊图像,利用L2范数正则化对模糊核进行估计;最后,利用超拉普拉斯先验和所估计的模糊核对清晰图像进行估计.与传统的方法相比,所提出的方法不需要多尺度迭代框架.实验结果表明,所提出的算法在获得较好的显著边缘结构以及清晰图像的同时,能够减少算法计算的时间.

Blur kernel estimation method based on deep encoder-decoder network

The significant edges-structure of blurry image plays an important role in estimation of blur kernel with the image deblurring problem.This paper proposes a method for estimating blur kernel based on deep encoder-decoder network.Firstly,after training by the constructing train dataset,the deep encoder-decoder network obtains the significant-edges of blurry image adaptively.And then,the blur kernel can be estimated by the L2 norm regularization where combining the significant edge-structures and the blurry image.Finally,the latent image can be estimated by using the hyper-Laplacian priors after estimating blur kernel.Comparing with the traditional methods,the proposed method does not use the multi-scale iterative framework to estimate kernel.Experimental results show that the proposed method can reduce the computation time of the algorithm while obtaining better significant edge-structures and latent image.