Blind deconvolution is a significant technology in the restoration of atmospheric turbulence-degraded images. However,
if the atmospheric turbulence-degraded images are contaminated by noise, the restoration images will be beyond real
image due to involving a mount of noise. A novel blind deconvolution method has been proposed. In this method, the
degraded image is preprocessed by a linear filter for reducing noise, and the filter is considered in the cost function of
blind deconvolution. An alternating minimization algorithm based on conjugate gradient method is applied for
minimizing the cost function. Thus, the smoothness induced by linear filter and the blur induced by atmospheric
turbulence are eliminated in blind deconvolution simultaneously. For verifying this method, the images degraded by
turbulence with atmospheric seeing parameter equal to 0.1 meters for 2 meters telescope and contaminated by noise with
signal noise ratio equal to 10 dB are simulated by computer and restored by this method. The experiment result
demonstrates that the noise is reduced without introducing any smoothing and the degraded image are restored
effectively. The image restored by this method is compared with by the blind deconvolution method based on edge
preserving regularization. The result shows that the effect of reducing noise of our method is better than the latter.
In this paper, a novel target tracking approach is presented. A scanning mirror has been applied to modulate object and background signal periodically. The object position can be derived by a formula composed of 1st-order and 2nd-order harmonic components obtained form output signals via Fourier transform. Because the field of view of optical system is very small, the radiation from background collected by four cells of quadrant detector doesn't change in short time. The
radiation of background can be suppressed. The result of experiment shows that this tracking approach has a high precision without the influence of the radiation from background and the energy distribution of image spot.
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