自适应非凸稀疏正则化下自适应光学系统加性噪声的去除

自适应光学系统可以实时测量并校正波前信息,但是系统中大量的噪声严重影响了系统的探测精度.自适应光学系统中一般为加性噪声,本文提出一种全新的变分处理模型去除加性噪声,该模型采用自适应非凸正则项.非凸正则项在保持图像细节上较凸正则项具有更好的效果,能更好地保持点源目标的完整性.另外,根据不同区域的噪声水平自适应地构建正则化参数,使不同区域的像素点受到不同程度的噪声抑制,可以更好地保持目标的边缘细节.在算法实现上,为了解决非凸正则项收敛性较差的缺陷,采用分裂Bregman算法及增广拉格朗日对偶算法进行计算.实验及数值仿真结果都表明,该方法能够较好地去除系统中的加性噪声,且光斑信号保存得较为完整,处理后的质心探测精度及信噪比较高.

Removal of additive noise in adaptive optics system based on adaptive nonconvex sparse regularization

Adaptive optics (AO) system which is widely used in astronomical observations can improve the image quality by the real-time measurement and correction of the wave-front. One of the main problems in the AO system is the poor quality of the image because of the system noises. The noises in AO system are additive noises. The main sources of the noises are the background noise, the photon noise, and the readout noise of charge-coupled device. The background noise is distributed evenly and is easy to process. The photon noise is dependent on the characteristics of the spot itself. Readout noise, which is Gaussian distribution with the mean value of 0 and the variance of σ², is the main noise source in AO system. In this paper, we focus on the readout noise and propose a new regularization model to remove additive noises from the AO system. In this model, the regularization parameters can be adaptively changed. A nonconvex regularization term is used to make the homogeneous region of the image smooth efficiently, while the integrity of the spot can be well restored. The properties of the regularization proposed are shown below. 1) The proposed nonconvex regularization term can act as the L₀ norm which is sparser than L₁ norm. 2) The proposed model can protect the edge of the spot from over smoothing. To prevent the edges from over smoothing, the regularization parameter must be an increasing function. Moreover, it converges to a constant so that it cannot affect the strong gradient of the image. 3) The regularization term proposed is nonconvex which is more sensible to the minor change of the image. Therefore, the edges of the image can be better preserved. Though the proposed model can well preserve the edges of the spot, it is difficult to resolve by traditional methods because of the nonconvexity. Split Bregman algorithm and augmented Lagrangian duality algorithm are used to solve this problem. We can obtain a denoised spot image as well as an edge indicator by using the proposed model. The visual and quantitative evaluations are used to value the restored images. The evaluating indicators are the peak signal-to-noise ratio and centroid detecting error which includes the root mean square and the peak valley value of the centroid deviation. The simulation and experimental results show the efficiency of this model in removing the additive noises from the AO system.