A blind identification algorithm based on the filtering in frequency domain for degraded images

When the degradation is mainly caused by the camera linear motion, or the out of focus lens of the imaging systems, the model of the degradation functions or known as the point spread function (PSF) can be established with the corresponding parameters. In the situations, the identification of the PSF can be converted to the identification of the parameter of the degradation functions. In order to increase the identification accuracy of the degradation function and the computational efficiency in the identification process, the low pass finite impulse response (FIR) filter which is named as the inverse adaptive amplitude spectrum filter (IAASF) proposed in this paper is applied to identify the PSF. First, the amplitude spectra of the degraded images are calculated with discrete fast Fourier transform (DFFT). Second, filtering the amplitude spectra with IAASF in order to accurately measure the distance of the characteristic curves on the filtered amplitude spectra image frames, i.e. measuring the distance of the central two spectra lines on amplitude spectra image frames for the linear motion blur, and measuring the diameter of the inner circle of the concentric circles on the amplitude spectra image frames for the defocus blur, thus, the parameter of the PSF can be determined. Therefore, the PSF can be obtained from the parametric model of degradation function. The original images have been restored by the identified degradation function and the degraded image with Wiener filtering. The experimental results show that the algorithm proposed in this paper has higher computational efficiency and the higher estimation accuracy of the degradation function while the degraded images have higher signal to noise ratio (SNR). The algorithm has certain reference significance for the restoration and identification of the turbulence degraded images yielded by the aero-optical effects.