Adaptive restoration method of multi-frame turbulence-degraded images based on stochastic point spread function

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 computational 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 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 relative error of the objective function was used as the iterative termination condition of the adaptive algorithm. Finally, 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 algorithm 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 restoration effect on turbulence-degraded images.