基于超拉普拉斯先验与核谱特性噪声图像盲去模糊

现有大部分盲图像去模糊方法对噪声敏感，即使少量的噪声可大大降低恢复图像的质量．考虑到模糊图像中同时隐含有清晰图像信息和模糊核信息，我们同时利用卷积核谱特性先验和清晰图像梯度域超拉普拉斯先验联合建立含噪图像盲去模糊模型，较单独使用卷积核先验与清晰图像先验建模更合理，也能获得更精确的估计图像．本文借助于Hessian矩阵，利用模糊图像及卷积核联合生成先验子，而非单独的估计图像先验子，建立优化模型．求解模型时，通过迭代策略交替细化模糊核和清晰图像．在清晰图像恢复阶段，因存在超拉普拉斯先验项，提出用变量分离法计算清晰图像．清晰图像采用快速傅里叶变换及封闭阈值公式求解，以提高优化速度．实验结果表明：与其他方法相比，本文方法能获得更鲁棒的模糊核和更精确的清晰图像，且收敛速度更快．

Noisy Image Blind Deblurring via Hyper Laplacian Prior and Spectral Properties of Convolution Kernel

Most of blind deblurring methods are sensitive to image noise. Even a small amount of noise can degrade the quality of restoration image dramatically. Considering that blurry image contains both blur kernel information and clear image information implicitly, we employ a prior of convolutional kernel spectral, in combination with a hyper Laplacian prior of clear image in gradient domain, to establish optimization model for blind noisy image deblurring. This model is more reasonable than other models which do not make full use of the blurry image information, so our model can obtain more accurate estimation image. In this paper, the Hessian matrix is employed to generate a prior term by using the blurry image and a blur kernel together instead of just the clear image. The proposed model can be solved by an iterative scheme which alternatively refines the blur kernel and the estimation image. At the latent image restoration stage, the variable splitting method is adopted to calculate the clear image because of the hyper Laplacian prior term. Furthermore, clear images are obtained by using fast Fourier transformation and closed-form threshold formulas to speed up the optimization process. Experimental results show that, compared with other methods, the proposed method can obtain more robust blur kernel and more accurate clear image, and the convergence speed is faster.