稀疏先验型的大气湍流退化图像盲复原

Blind restoration of atmospheric turbulence degraded images by sparse prior model

图像盲复原是仅从降质图像就恢复出模糊核和真实锐利图像的方法,由于其病态性,通常需要加入图像先验知识约束解的范围。针对传统的图像梯度l2和l1范数先验不能真实刻画自然图像梯度分布的特点,本文将图像梯度稀疏先验应用于单帧大气湍流退化图像盲复原中。先估计模糊核再进行非盲复原,利用分裂Bregman算法求解相应的非凸代价函数。仿真实验表明,与总变分先验(l1范数)相比,稀疏先验有利于模糊核的估计、产生锐利边缘和去除振铃等,降低了模糊核的估计误差从而提高了复原质量。最后对真实湍流退化图像进行了复原。

Blind image deconvolution is one method of restoring both kernel and real sharp image only from degraded images,due to its illness,image priors are necessarily applied to constrain the solution.Given the fact that traditional image gradient l2 and l1 norm priors cannot describe the gradient distribution of natural images,in this paper,the image sparse prior is applied to the restoration of single-frame atmospheric turbulence degraded images.Kernel estimation is performed first,followed by non-blind restoration and the split Bregman algorithm is used to solve the non-convex cost function.Simulation results show that compared with total variation priori,sparse priori is better at kernel estimation,producing sharp edges and removal of ringing,etc.,which reducing the kernel estimation error and improving restoration quality.Finally,the real turbulence-degraded images are restored.