An efficiency restoration method for turbulence-degraded image base on improved SeDDaRA method

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Turbulence-degraded image restoration is an important part in detection system which based on image. Most of current researches on turbulence-degraded image were focus on getting perfect image and not very care about processing speed. They are not acceptable when they apply on a real-time detection system. In order to restore degraded image clearly and rapidly, in this paper we introduce an efficiency restoration method for turbulence-degraded image base on improved SeDDaRA (self-deconvolving data reconstruction algorithm) method. SeDDaRA transform the image data form space field to spectrum field and smooth image's spectrum data and use a power law relation applied to the smoothed spectrum data to extract a filter function. This filter function can be used to restore and enhance higher-frequency content and get the system's Point Spread Function (PSF). The PSF can be used for deconvolution filter such as Winner and nonnegative least squares to restore the image. There are three major contributions in this paper. Firstly, we add a pre-denoise process to remove the noise which introduced by system such as period noise and Gauss noise. This step can significant improve the restore image's quality. Secondly we use an optimum method to extract the filter function which responded to PSF. The method, based on spectrum's power law characteristic, only need compute 8-direction date of the whole data to get the parameter. Compared with normal SeDDaRA method which need compute all data in spectrum field the new method can significant reduce the compute complication. Thirdly we utilize image's inherent characteristic and introduce a novel method to estimation deconvolution filter's SNR. The accurate SNR can efficiently improve the restoration quality. Compared with other restoration method, our method is noniterative and requires only that the point-spread function be space invariant and the transfer function be real. These mean that our method can work efficiently and requires little knowledge of the original data or the degradation. Experiments on real turbulence-degraded image indicate that the proposed method is very fast and can get quality restore image, which demonstrates the feasibility and validity of the proposed method.