一种基于改进的暗通道先验的运动模糊核估计方法

传统的暗通道先验已成功地运用于单一图像去模糊问题,但是,当模糊图像具有显著噪声时,暗通道先验无法对模糊核估计起到作用.因此,得益于分数阶计算能够有效地抑制信号的噪声并对信号的低频部分进行增强,将分数阶计算理论与模糊图像的暗通道先验相结合,提出一种基于改进的暗通道先验的运动模糊核估计方法.首先,结合最大后验估计算法与分数阶暗通道先验,构建出运动模糊图像的核估计模型;其次,利用半二次方分裂法解决模型的非凸问题;最后,根据粗糙-精细的策略,利用多尺度迭代框架估计出准确图像的模糊核,进而利用非盲去模糊的方法求解清晰图像.实验结果表明:在有无显著噪声的模糊图像中,所提出的算法虽然所需计算时间较长,但是能够获得较为准确的模糊核,并且能够减少图像噪声以及振铃伪影,提高清晰图像估计的质量;此外,对于不同类型的模糊图像,所提出的算法也同样适用.

A method of motion blurry kernel estimation based on improved dark channel prior

The traditional dark channel prior has been successfully applied to the single image deblurring problem. However, the dark channel prior is ineffective for the blur kernel estimation, when the blurry image has significant noise. Motivated by the success of the fractional-order calculation which can nonlinearly suppress the noise of the signal and enhance the low-frequency part of the signal, the fractional-order calculation theory is combined with the dark channel prior. And then, this paper proposes a method of motion blur kernel estimation based on an improved dark channel prior. Firstly, combined with the maximum posterior estimation algorithm and the fractional order dark channel prior, the blur kernel estimation model is constructed. Then, the half-quadratic splitting method is used to solve the non-convex problem of the model. Finally, according to the coarse-fine strategy, the multi-scale iterative framework is used to iteratively estimate the blur kernel and the latent image is estimated by the non-blind image deblurring method. Experimental results show that the proposed algorithm can obtain a better blur kernel in the blurry image with or without significant noise, and can reduce image noise and ringing artifacts, and improve the quality of the latent image estimation, although it need more running time for processing one image. Meanwhile, the proposed method can be used to process different types of blurry image.