利用低秩先验的噪声模糊图像盲去卷积

摘要

单幅图像盲去卷积的目的是从一幅观测的模糊图像估计出模糊核和清晰图像.该问题是严重病态的,尤其是观测图像中噪声不可忽略时更具挑战性.该文主要针对如何有效利用低秩先验约束进行噪声模糊图像盲去卷积问题,提出一种在交替最大后验(MAP)估计框架下利用低秩先验约束的单幅噪声模糊图像盲去卷积方法.首先,在估计中间复原图像时,利用低秩先验约束对复原图像中的噪声进行抑制.然后,采用降噪后的中间复原图像估计模糊核,得到更好质量的模糊核估计.迭代上述两个操作获得最终可靠的模糊核估计.最后,根据所估计的模糊核,通过非盲去卷积方法复原出清晰图像.实验结果表明:所提方法在定量和定性评价指标上优于已有的代表性方法.%The purpose of single image blind deconvolution is to estimate the unknown blur kernel from a single observed blurred image and recover the original sharp image. Such a task is severely ill-posed and even more challenging especially in the condition that the noise in the input image can not be negligible. In this paper, the main problem this study focuses on is how to effectively apply low rank prior to blind deconvolution. A single noisy and blurry image blind deconvolution algorithm is proposed, using alternating Maximum A Posteriori (MAP) estimation combined with low rank prior. First, when estimating the intermediate latent image, low rank prior is used as the constraint that is used for noise suppression of the restored image. Then the denoised intermediate latent image in turn leads to higher quality blur kernel estimation. These two operations are iterated in this manner to arrive at reliable blur kernel estimation. Finally, the non-blind deconvolution method is chosen to be used with sparse prior knowledge to achieve the final latent image restoration. Extensive experiments manifest the superiority of the proposed method over state-of-the-art techniques, both qualitatively and quantitatively.