基于噪声与图像同步迭代来确定时间步进法的规整化参数

为了在反卷积过程中正确地估计噪声的方差，该文构造一幅纯噪声图像跟实际的观测图像同步进行反卷积计算，并把纯噪声图像的方差作为观测图像中噪声方差的估计值来辅助计算规整化参数。针对规整化的各项异性，该文提出了能够保持两种噪声同步变化的特殊的规整化项。新的规整化项在迭代纯粹噪声图像时使用，这样确保每次迭代都可以保持人工噪声与实际图像噪声的统计特性相一致。在能够准确知道迭代过程中图像包含噪声的方差的时候，该文建立了规整化参数与图像噪声方差之间的关系式并转化成简单的解一元二次方程问题。实验证明新的算法不但更好地抑制了噪声而且避免了过平滑，基于时间步进法计算变分图像恢复的适应性被明显的提高了。

Selecting Regularization Parameter in Time Marching Method Based on the Synchronous Iteration of Noise and Image

In order to correctly estimate the variance of noise in iteration, a pure synthesis noise as an image is synchronously iterated with the observation image in de-convolution, and it takes variance of pure noise image as the estimation of the variance of noise in observation image and computes the regularization parameter by the variance. A novel regularization term that can ensure the synchronous changing of the variance of the two noises is proposed in this article. The new regularization term is put into use only in iteration of pure noise image. Under the condition of knowing the variance of noise of image in iteration, this paper established the relationship between the variance of synthetic noise and the regularization parameter, and the relationship was converted to a simple quadratic equation. Experiments confirm that the new algorithm not only better restrains the noise but also avoids the over smoothing. The adaptability of total variation based image restoration is improved.