基于Hessian矩阵范数正则化方法的共聚焦图像复原

对于泊松噪声污染下的模糊共聚焦图像复原问题,为解决传统方法中存在的阶梯效应,提出了一种基于Hessian矩阵范数的正则化方法.在泊松概率模型的基础上,该方法引入Hessian矩阵范数作为正则条件,并应用交替方向乘子法和梯度投影方法求解最优化模型.在激光扫描共聚焦显微镜实验中,所获得的复原图像质量优于传统方法,此结果证明了该方法可以有效地复原泊松噪声污染下的模糊共聚焦图像.

Confocal Image Restoration Based on Hessian Matrix Norm Regularization Algorithm

For the problem of deblurred confocal image restoration under Poisson noise, a regularization method based on Hessian matrix norm was proposed to solve the stairs effect existing in traditional methods. Based on the Poisson probability model, the method introduced the Hessian matrix norm as a regular condition, and applied the alternating direction multiplier method and gradient projection method to solve the optimization model. The quality of the reconstructed image obtained in the confocal laser scanning microscopy experiment was better than that of the traditional method. This result proves that the method can effectively restore the deblurred confocal image under Poisson noise.