A novel algorithm based on image quality assessment was proposed for a turbulence-degraded infrared image to deblur the fuzzy infrared image caused by a high-speed turbulent flow field.Firstly,the degradation process was simplified as parameter-describing 2-D Gaussian function according to the prior knowledge, the degraded image was segmented into edge region, texture region and plain region and the weighted average of those regional 2-D kurtosis were used as the image kurtosis. Then, the kurtosis of restored image varying with the parameter under different support regions was calculated and the curvature-maximum criterion was used to estimate the corresponding parameter from the “kurtosis-parameter” curve. After that, the Point Spread Function(PSF) determined by the support domain and corresponding estimated parameter were used to restore the degraded image. Finally, a no-reference image quality assessment was used to compare different restored images,and the PSF of the recovered image with the highest quality was regarded as a final identification result. Experimental results show that the proposed algorithm can identify the parameter and support region of the blur function well, and the maximum deviation of the estimated parameter and the real value is less than ±5% when the Signal to Noise Ratio(SNR) of the degraded image is larger than 30 dB. The identification results can be used as an initial PSF estimation for other turbulence-degraded infrared image restoration algorithms.