目前,基于统计先验的图像去模糊方法对噪声敏感,细节恢复能力有限,而基于先验学习的算法对图像及其模糊类型、噪声水平等适应性较差。针对上述问题,基于图像模糊前后像素直方图统计,首先提出一种简单有效的低值像素先验。然后针对现有方法对图像去模糊后出现大量噪声或伪影等问题,设计深度卷积神经网络学习图像深度去噪先验,并联合低值像素先验、梯度稀疏先验提出新的去模糊模型。同时,在模糊核估计过程中,利用图像分解方法分离出图像的结构层,并在结构层估计模糊核,获得更为准确的估计结果。大量实验结果表明,本文算法不仅具有很好的细节恢复能力,且对图像及其模糊类型、噪声水平等更具稳健性。与现有主流算法相比,本文方法优势明显。

The traditional blind image deblurring algorithm based on the statistical prior models has the disadvantages of sensitivity to noise and limited detail recovery, while the learning-based image deblurring algorithm has poor adaptability for blurring kernel and noise level. To address the above problems, we propose a simple and effective low pixel sparse prior based on the statistical differences between the histograms of original and blurred images first. Then, in order to remove the noises and artifacts in restored image, a deep convolution neural network is designed to learn image denoising prior, which combines low pixel sparse prior and gradient sparse prior to form a new image deblurring model. Meanwhile, we estimate the blurring kernel in the structure layer so as to get a more accurate one, and the structure layer can be obtained by the image decomposition method. Numerous experimental results show that the proposed algorithm can restore more image details, and show more robustness to image type, blurring kernel type and noise level. The proposed method outperforms other recent state-of-the-art related approaches.