He Tao, Hu Jie, Huang Haiqing
Appl Opt. 2018 Dec 10;57(35):10243-10256. doi: 10.1364/AO.57.010243.
Images obtained by photon-counting sensors are always contaminated with Poisson noise. Total variation (TV) has been extensively researched in image deconvolution because of its remarkable ability to preserve details. However, TV is based on the requirement that the global image gradient obeys a Laplacian distribution and can hardly maintain the information of each part of the image. We extended the global TV to nonlocal modeling and established an intensity-adaptive nonlocal regularization based on similar blocks. Meanwhile, to restrain the staircase effect caused by first-order regularization, we proposed a new hybrid nonlocal regularization by modeling the sparsity of the high-order derivative. An efficient alternating direction method of multipliers algorithm was employed to solve the proposed model, and the adaptive selection strategy of regularization parameters in the model was further studied and analyzed. The experimental results show that the proposed hybrid high-order nonlocal gradient sparsity regularization model achieves a substantial computational time improvement compared to another nonlocal restoration algorithm while producing a relatively clear recovery image.
通过光子计数传感器获得的图像总是受到泊松噪声的污染。由于其在保留细节方面的卓越能力，全变差（TV）在图像去卷积中得到了广泛研究。然而，TV基于全局图像梯度服从拉普拉斯分布的要求，并且很难保留图像各部分的信息。我们将全局TV扩展到非局部建模，并基于相似块建立了强度自适应非局部正则化。同时，为了抑制一阶正则化引起的阶梯效应，我们通过对高阶导数的稀疏性进行建模，提出了一种新的混合非局部正则化。采用一种高效的交替方向乘子算法来求解所提出的模型，并对模型中正则化参数的自适应选择策略进行了进一步研究和分析。实验结果表明，与另一种非局部恢复算法相比，所提出的混合高阶非局部梯度稀疏正则化模型在计算时间上有显著改善，同时能产生相对清晰的恢复图像。
