We propose a speckle imaging algorithm in which we use the improved form of spectral ratio to obtain the Fried parameter, we also use a filter to reduce the high frequency noise effects. Our algorithm makes an improvement in the quality of the reconstructed images. The performance is illustrated by computer simulations.
We demonstrate a maximum a posteriori (MAP) blind image deconvolution algorithm with bandwidth over-constrained and total variation (TV) regularization to recover a clear image from the AO corrected images. The point spread functions (PSFs) are estimated by bandwidth limited less than the cutoff frequency of the optical system. Our algorithm performs well in avoiding noise magnification. The performance is demonstrated on simulated data.
This paper presents a technique that performs multi-frame super-resolution of differently exposed images. The method first employs a coarse-to-fine image registration method to align image in both spatial and range domain. Then an image fusion method based on the maximum a posterior (MAP) is used to reconstruct a high-resolution image. The MAP cost function includes a data fidelity term and a regularized term. The data fidelity term is in the L2 norm, and the regularized term employs Huber-Markov prior which can reduce the noise and artifacts while reserving image edges. In order to reduce the influence of registration errors, the high-resolution image estimate and registration parameters are refined alternatively by minimizing the cost function. Experiments with synthetic and real images show that the photometric registration reduce the grid-like artifacts in the reconstructed high-resolution image, and the proposed multi-frame super resolution method has a better performance than the interpolation-based method with lower RMSE and less artifacts.
This paper describes an approach to reconstructing wavefronts on finer grid using the frozen flow hypothesis (FFH), which exploits spatial and temporal correlations between consecutive wavefront sensor (WFS) frames. Under the assumption of FFH, slope data from WFS can be connected to a finer, composite slope grid using translation and down sampling, and elements in transformation matrices are determined by wind information. Frames of slopes are then combined and slopes on finer grid are reconstructed by solving a sparse, large-scale, ill-posed least squares problem. By using reconstructed finer slope data and adopting Fried geometry of WFS, high-resolution wavefronts are then reconstructed. The results show that this method is robust even with detector noise and wind information inaccuracy, and under bad seeing conditions, high-frequency information in wavefronts can be recovered more accurately compared with when correlations in WFS frames are ignored.
This paper presents a technique that performs coarse-to-fine image registration both in spatial and range domain. The goal of image registration is to estimate geometric and photometric parameters via minimization of an objective function in the least square sense. In order to reduce the probability of falling into a local optimal solution, the algorithm employs a coarse-to-fine strategy. In the coarse step, an illumination offset and contrast invariant feature detector which is named SURF is used to estimate affine motion parameters between the reference image and the target image, and then the intensity of corresponding pixels is used to directly estimate contrast and bias parameters based on RANSAC. In the fine step, the estimated parameters obtained in the coarse step are used as a good initial estimation, and photometric and affine motion parameters are refined alternatively via minimizing the objective function. Experiments on simulated and real images show that the proposed image registration method is superior to the feature-based method used in the coarse step and the groupwise image registration algorithm proposed by Bartoli.
Shift-invariant motion blur can be modeled as a convolution of the true latent image and the blur kernel with additive noise. Blind motion de-blurring estimates a sharp image from a motion blurred image without the knowledge of the blur kernel. This paper proposes an improved edge-specific motion de-blurring algorithm which proved to be fit for processing remote sensing images. We find that an inaccurate blur kernel is the main factor to the low-quality restored images. To improve image quality, we do the following contributions. For the robust kernel estimation, first, we adapt the multi-scale scheme to make sure that the edge map could be constructed accurately; second, an effective salient edge selection method based on RTV (Relative Total Variation) is used to extract salient structure from texture; third, an alternative iterative method is introduced to perform kernel optimization, in this step, we adopt l<sub>1</sub> and l<sub>0</sub> norm as the priors to remove noise and ensure the continuity of blur kernel. For the final latent image reconstruction, an improved adaptive deconvolution algorithm based on TV-l<sub>2</sub> model is used to recover the latent image; we control the regularization weight adaptively in different region according to the image local characteristics in order to preserve tiny details and eliminate noise and ringing artifacts. Some synthetic remote sensing images are used to test the proposed algorithm, and results demonstrate that the proposed algorithm obtains accurate blur kernel and achieves better de-blurring results.
An imaging system is constructed by atmosphere turbulence and ground-based telescope when the latter is used to observe a space object. The wavefront measurement produced by adaptive optics system can be used to estimate the point spread function (PSF) of the imaging system since it contains the wavefront aberration information of the light from the object. But the detector noise of the wavefront sensor (WFS) will inevitably bring estimation error. Based on the statistical theory, a method is presented to improve the PSF estimation accuracy by eliminating the noise error from the wavefront measurement. The numerical simulation shows that the estimation error of this method could be lower than 10%. It also indicates that the higher the signal-noise ratio (SNR) of the WFS is, the more frames of the wavefront measurements are used, and the bigger the Fried constant is, the more accurate the estimation will be. The work in this paper can be applied to performance evaluation of imaging system, deconvolution of AO images, as well as photometric analysis of space object.
In adaptive optics (AO) system, the detector noise is one of the main error sources of Shack-Hartmann wavefront sensor (SH-WFS). Based on the statistical analysis of the noise, a noise error estimation method is presented by using multiframe of the Hartmann spots pattern and the centroid displacements calculated from them. A numerical simulation system for wavefront measuring is built, and used to verify the validity of this method. It shows that the estimation error of this method could be lower than 2%, provided that the signal-noise-ratio (SNR) is sufficient for the WFS working normally. We studied the least frames of data that are required for the method when the SNR of the WFS is at different levels. It indicates that fewer frames are required as the SNR level is higher, and only 2 frames of data are required when the SNR level is high enough. For different types of detector noise, we have analyzed the influence of the accuracy of their prior information on the estimation error. It shows that the influence of the readout noise is strong, and the influence of the photon-noise, the dark-current noise and the sky-background noise is neglectable, since the WFS is usually exposed shortly. The work in this paper can be of certain significance in estimating the point spread function of AO system with the WFS measurements.