为了改善已恢复图像的图像质量,提出一种基于变分模型的图像复原算法.首先通过多曝光图像成像算法重建出对于光照具有线性响应的亮度图像,并采用泊松-高斯噪声模型对图像退化过程进行建模;然后通过分析亮度图像的同态子块,将泊松-高斯噪声模型的未知参数估计问题转化为求解一个线性方程组;再根据贝叶斯公式和最大后验概率准则推导出一个具有严格凸函数性质的变分模型,其最优解即为对于无噪图像的最优估计;最后利用最小二乘法以及最速下降法分别对噪声的参数以及无噪图像进行估计.实验结果表明,本文算法能够在抑制噪声的同时保证图像的细节与图像质量.%In order to improve the quality of recovered image, this paper proposes an image denoising algo-rithm based on variational model. Firstly, multiple-exposure imaging method is used to recovery radiance map which represents the linear response of light intensity. Simultaneously, Poisson-Gaussian noise model-ing is introduced for modeling the degraded image. Afterwards, by making an analysis to the homogeneous patches of radiance map, a linear system of equations is derived to estimate the parameters of Poisson- Gaussian noise modeling. Moreover, following Bayes's rule and maximum a posteriori, a variational model yields to strictly convex function is derived which the optimal solution is the recover image. Eventually, Unknown parameters and the optimal solution are derived through least square method and steepest descent method respectively. Experiment results show that our algorithm is capable to remove noise from degraded image, meanwhile the detail and quality are guaranteed.
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