暗通道约束和交替方向乘子法优化的湍流图像盲复原
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摘要
为提高湍流退化图像的复原效果,针对盲复原算法在最大后验概率框架下,使用梯度分布先验信息约束容易求得模糊平凡解的问题,提出了一种暗通道约束和交替方向乘子法优化的湍流图像盲复原算法。基于多尺度的思想,在每一层尺度上,对图像施加暗通道先验约束,对点扩散函数施加非负性约束和能量约束。对采用坐标下降法交替迭代估计当前尺度下的模糊核和图像,当达到最大尺度时,得到最终估计的模糊核。结合总变分模型,采用交替方向乘子法优化实现图像细节快速恢复。实验结果表明,新算法使用的先验信息约束,有利于得到清晰解,在总变分模型下能收敛到全局最优解,可以有效抑制图像复原过程中产生的伪迹,恢复出更好的目标图像细节。
In order to improve the effect of turbulence degraded image restoration,aiming at the problem that the fuzzy solution is easy to be obtained by using the prior information constraint of gradient distribution under the framework of maximum a posteriori probability of blind restoration algorithm,this paper proposes a dark channel constraint and alternated direction multiplier optimization of turbulence degraded image blind restoration method.First,based on the idea of multi-scale,a dark channel prior constraint is imposed on the image and non-negative constraints and energy constraints are imposed on the point spread function at each level.Then,the kernel and image of the current scale are estimated by alternating iterations of coordinate descent method. When the maximum scale is reached,the final estimated blur kernel is obtained. Last,combined with the total variational model,the image details are quickly restored using the alternate direction optimization method. The experimental results show that the prior information constraint used in the proposed algorithm is advantageous to obtain a clear solution,and can converge to the global optimal solution in the total variational model,which can effectively suppress the artifacts produced in the image restoration process and recover a better target image detail.
In order to improve the effect of turbulence degraded image restoration,aiming at the problem that the fuzzy solution is easy to be obtained by using the prior information constraint of gradient distribution under the framework of maximum a posteriori probability of blind restoration algorithm,this paper proposes a dark channel constraint and alternated direction multiplier optimization of turbulence degraded image blind restoration method.First,based on the idea of multi-scale,a dark channel prior constraint is imposed on the image and non-negative constraints and energy constraints are imposed on the point spread function at each level.Then,the kernel and image of the current scale are estimated by alternating iterations of coordinate descent method. When the maximum scale is reached,the final estimated blur kernel is obtained. Last,combined with the total variational model,the image details are quickly restored using the alternate direction optimization method. The experimental results show that the prior information constraint used in the proposed algorithm is advantageous to obtain a clear solution,and can converge to the global optimal solution in the total variational model,which can effectively suppress the artifacts produced in the image restoration process and recover a better target image detail.
引文
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[12]Xu L,Zheng S,Jia J.Unnatural L0Sparse Representation for Natural Image Deblurring[C]∥Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition,2013:1107-1114
[13]Pan J,Hu Z,Su Z,et al.Deblurring Text Images via L0-Regularized Intensity and Gradient Prior[C]∥Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition,2014:2901-2908
[14]Zhu X,Milanfar P.Removing Atmospheric Turbulence via Space-Invariant Deconvolution[J].IEEE Trans on Pattern Analysis&Machine Intelligence,2013,35(1):157-70
[2]Ayers G R,Dainty J C.Interative Blind Deconvolution Method and its Applications[J].Optics Letters,1988,13(7):547-549
[3]Fergus R,Singh B,Hertzmann A,et al.Removing Camera Shake from a Single Photograph[J].ACM Trans on Graphics,2006,25(25):787-794
[4]Krishnan D,Fergus R.Fast Image Deconvolution Using Hyper-Laplacian Priors[C]∥International Conference on Neural Information Processing Systems,2009:1033-1041
[5]Krishnan D,Tay T,Fergus R.Blind Deconvolution Using a Normalized Sparsity Measure[C]∥Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition,2011:233-240
[6]Pan J,Sun D,Pfister H,et al.Blind Image Deblurring Using Dark Channel Prior[C]∥Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition,2016:1628-1636
[7]Cho S,Lee S.Fast Motion Deblurring[J].ACM Trans on Graphics,2009,28(5):89-97
[8]李晖晖,钱林弘,杨宁,等.基于边缘预测和稀疏约束的湍流图像盲复原[J].仪器仪表学报,2015,36(4):721-728Li Huihui,Qian Linhong,Yang Ning,et al.Turbulence Degraded Image Blind Restoration Based on Sparity Regularization and Edge Prediction[J].Chinese Journal of Scientific Instrument,2015,36(4):721-728(in Chinese)
[9]Boyd S,Parikh N,Chu E,et al.Distributed Optimization and Statistical Learning via the Alternating Direction Method of Multipliers[J].Foundations&Trends in Machine Learning,2010,3(1):1-122
[10]Ren D,Zhang H,Zhang D,et al.Fast Total-Variation Based Image Restoration Based on Derivative Alternated Direction Optimization Methods[J].Neurocomputing,2015,170:201-212
[11]He K,Sun J,Tang X.Single Image Haze Removal Using Dark Channel Prior[C]∥Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition,2009:1956-1963
[12]Xu L,Zheng S,Jia J.Unnatural L0Sparse Representation for Natural Image Deblurring[C]∥Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition,2013:1107-1114
[13]Pan J,Hu Z,Su Z,et al.Deblurring Text Images via L0-Regularized Intensity and Gradient Prior[C]∥Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition,2014:2901-2908
[14]Zhu X,Milanfar P.Removing Atmospheric Turbulence via Space-Invariant Deconvolution[J].IEEE Trans on Pattern Analysis&Machine Intelligence,2013,35(1):157-70