改进的迭代算法在图像恢复正则化模型中的应用

The Application of Improved Iterative Algorithm to Regularization Model of Image Restoration

根据图像成像过程容易受泊松噪声的影响,提出用Kullback-Leibler距离描述保真项,用平方根复合函数描述正则项,建立具有自适应权系数的能量泛函正则化模型.由于模型的梯度退化和海森矩阵的规模较大,使得无法应用牛顿迭代算法.本文利用退化梯度幅值作为约束集,建立可对角化和容易求逆的海森矩阵,提出改进的牛顿投影迭代算法.仿真表明,该方法取得较小的相对误差、偏差,较高的信噪比和良好的视觉效果.

According to the imaging process is easily affected by Poisson noise,the image restoration regularization model that fidelity term is described by Kullback-Leibler Euclidean and the regularization term is established by the square root compound function,with adaptive weight coefficients,is proposed. For the gradient degeneration and the large scale Hessian matrix,it is unable to apply the Newton iterative algorithm to the model. In this paper,constraint set is introduced by the magnitude value of degeneration gradient,the diagonal and easily computed Hessian matrix is established,and the improved Newton iterative projection algorithm is proposed. Simulation results showthe proposed can effectively restore image,such as the lower relative error and deviation,the higher peak signal to noise ratio,and better visual effect.