中国光学Issue(3)：368-377,10.DOI:10.3788/CO.20150803.0368
基于随机点扩散函数的多帧湍流退化图像自适应复原方法
Adaptive restoration method of multi-frame turbulence-degraded images based on stochastic point spread function
摘要
Abstract
As the turbulence-degraded images are stochastic, an adaptive restoration approach of multi-frame turbulence-degraded images was proposed based on stochastic Point Spread Function( PSF) .Firstly, an image degradation model of stochastic PSF was introduced, and the influence of the model on the image restoration was analyzed.The degradation model of multi-frame images based on stochastic PSF was established.On this basis, the TV restoration model based on multi-frame images was established.In order to improve the compu-tational efficiency of the algorithm, the model was solved by Forward-Backward Splitting( FBS) operator.Then a new adaptive selection method of regularization parameter was proposed.When the regularization parameter <br> which was calculated by the objective function of the TV model was convergent, the Peak Signal-to-Noise Ratio ( PSNR) of restoration image reached the maximum value.In order to get the best restoration effect, the rela-tive error of the objective function was used as the iterative termination condition of the adaptive algorithm.Fi-nally, the number of degraded images should be no more than 10 frames through the experimental analysis. Experimental results show that the ISNR of the AFBS algorithm has increased 1.4 dB more than the FBS algo-rithm based on single frame while the computing time is comparative when the number of degraded images was 10 frames.The proposed algorithm has an obvious inhibition on the noises, and it can obtain a better restora-tion effect on turbulence-degraded images.关键词
图像复原/自适应正则化/随机点扩散函数/多帧模型/前向后向分裂算子/湍流退化图像Key words
image restoration/adaptive regularization/stochastic point spread function/multi-frame model/for-ward-backward splitting/turbulence-degraded image分类
信息技术与安全科学引用本文复制引用
朱瑞飞,魏群,王超,贾宏光,吴海龙..基于随机点扩散函数的多帧湍流退化图像自适应复原方法[J].中国光学,2015,(3):368-377,10.基金项目
装备预研基金资助项目 （）
