KEYWORDS: Data modeling, Remote sensing, Databases, Data storage, Image storage, Image compression, Systems modeling, Lithium, Data centers, Image retrieval
Owing to the rapid development of earth observation technology, the volume of spatial information is growing rapidly; therefore, improving query retrieval speed from large, rich data sources for remote-sensing data management systems is quite urgent. A global subdivision model, geographic coordinate subdivision grid with one-dimension integer coding on 2n-tree, which we propose as a solution, has been used in data management organizations. However, because a spatial object may cover several grids, ample data redundancy will occur when data are stored in relational databases. To solve this redundancy problem, we first combined the subdivision model with the spatial array database containing the inverted index. We proposed an improved approach for integrating and managing massive remote-sensing data. By adding a spatial code column in an array format in a database, spatial information in remote-sensing metadata can be stored and logically subdivided. We implemented our method in a Kingbase Enterprise Server database system and compared the results with the Oracle platform by simulating worldwide image data. Experimental results showed that our approach performed better than Oracle in terms of data integration and time and space efficiency. Our approach also offers an efficient storage management system for existing storage centers and management systems.
Hyperspectral Imaging Systems (HIS) are widely applied in many fields. However, in the traditional design of HIS, it is much time-consuming to acquire an integrated hyperspectral image. Compressive sensing is an efficient method to process sparse data, and a single-pixel camera which used the digital micromirror device (DMD) for accomplishing the CS algorithms had been developed. Nowadays, DMD achieved great development. The size of mirror array is increasing while switch speed of a single mirror becomes very fast. Consequently, researchers make efforts to design a HIS using CS method. CS method is a method to scale down the spatial information but the hyperspectral datacubes are still huge because of the thousands of bands. In this paper, we design a DMD-based spectrometer architecture using the method of compressed sensing principle, combined with DMD's spectral filter characteristics. In the new architecture, there are two DMDs. One is used for implementing the CS pattern, the other for filtering the bands. It has spectral simply adjustable advantages. With this new technology, we can reduce the amount of information which needs to be transmitted and processed in both spatial and spectral domain. We also present some simulation results of implementation procedures.
In order to enhance the performance of adaptive optics image restoration, a novel wavefront reconstruction algorithm was presented that was based on generalized ridge estimation.
The performance of high-resolution imaging with large optical instruments is severely limited by atmospheric turbulence. Adaptive
optics (AO) offers a real-time compensation for turbulence. However, the correction is often only partial, and image restoration is
required for reaching or nearing to the diffraction limit. Wavelet-based techniques have been applied in atmospheric turbulencedegraded
image restoration. However, wavelets do not restore long edges with high fidelity while curvelets are challenged by small
features. Loosely speaking, each transform has its own area of expertise and this complementarity may be of great potential. So, we
expect that the combination of different transforms can improve the quality of the result. In this paper, a novel deconvolution
algorithm, based on both the wavelet transform and the curvelet transform (NDbWC). It extends previous results which were obtained
for the image wavelet-based restoration. Using these two different transformations in the same algorithm allows us to optimally detect
in tire same time isotropic features, well represented by the wavelet transform, and edges better represented by the curvelet transform.
The NDbWC algorithm works better than classical wavelet-regularization method in deconvolution of the turbulence-degraded image
with low SNR.
mage of objects is inevitably encountered by space-based working in the atmospheric turbulence environment, such as
those used in astronomy, remote sensing and so on. The observed images are seriously blurred. The restoration is
required for reconstruction turbulence degraded images. In order to enhance the performance of image restoration, a
novel enhanced nonnegativity and support constants recursive inverse filtering(ENAS-RIF) algorithm was presented,
which was based on the reliable support region and enhanced cost function. Firstly, the Curvelet denoising algorithm was
used to weaken image noise. Secondly, the reliable object support region estimation was used to accelerate the algorithm
convergence. Then, the average gray was set as the gray of image background pixel. Finally, an object construction limit
and the logarithm function were add to enhance algorithm stability. The experimental results prove that the convergence
speed of the novel ENAS-RIF algorithm is faster than that of NAS-RIF algorithm and it is better in image restoration.
A novel multi-limit unsymmetrical iterative blind deconvolution(MLIBD) algorithm was presented to enhance the performance of adaptive optics image restoration.The algorithm enhances the reliability of iterative blind deconvolution by introducing the bandwidth limit into the frequency domain of point spread(PSF),and adopts the PSF dynamic support region estimation to improve the convergence speed.The unsymmetrical factor is automatically computed to advance its adaptivity.Image deconvolution comparing experiments between Richardson-Lucy IBD and MLIBD were done,and the result indicates that the iteration number is reduced by 22.4% and the peak signal-to-noise ratio is improved by 10.18dB with MLIBD method. The performance of MLIBD algorithm is outstanding in the images restoration the FK5-857 adaptive optics and the double-star adaptive optics.
The observed object images are seriously blurred because of the influence of atmospheric turbulence. The deconvolution
is required for object reconstruction from turbulence degraded images. The wavelet transform provides a multiresolution
approach to image analysis and processing. We consider a wavelet-based adaptive edge-preserving
regularization deconvolution (WbARD) scheme for image restoration problems. This is accomplished by first casting the
classical image restoration problem into the wavelet domain. We consider the behavior of the blur operator in the atrous
wavelet domain. Then, we are able to adapt quite easily to scale-varying and orientation-varying features in the image
while simultaneously retaining the edge preservation properties of the regularization. Experimental results show that the
WbARD algorithm produces good performance in comparison to standard direct restoration approaches for turbulencedegraded
images.
Phase retrieval technique is one of the most important methods to measure the wavefront in adaptive optics. In this paper,
a linear phase retrieval (LPR) technique used in close-loop adaptive optics (AO) is introduced. The performance of a
close-loop AO system based on the LPR sensor is researched using numerical simulations first. Then an AO
experimental system based on LPR sensor is set up with a 32-element piezoelectric deformable mirror (DM). The static
phase aberration correction experiment is carried out to research the valid range of phase aberration that can be corrected
and the dynamic characteristic. Both the numerical simulation results and the experimental results show that the LPR
technique can be used in adaptive optics to correct the small phase aberration successfully. The dynamic characteristic
shows that the LPR sensor may be used in real-time AO system in future.
The performance of high-resolution imaging with large optical instruments is severely limited by atmospheric
turbulence. Adaptive optics (AO) offers a real-time compensation for turbulence. However, the correction is often only
partial, and image restoration is required for reaching or nearing to the diffraction limit. In this paper, we consider a
hybrid Curvelet-Fourier regularized deconvolution (HCFRD) scheme for use in image restoration problems. The
HCFRD algorithm performs noise regularization via scalar shrinkage in both the Fourier and Curvelet domains. The
Fourier shrinkage exploits the Fourier transform's economical representation of the colored noise inherent in
deconvolution, whereas the curvelet shrinkage exploits the curvelet domain's economical representation of piecewise
smooth signals and images. We derive the optimal balance between the amount of Fourier and Curvelet regularization by
optimizing an approximate mean-squared error (MSE) metric and find that signals with more economical curvelet
representations require less Fourier shrinkage. HCFRD is applicable to all ill-conditioned deconvolution problems, its
estimate features minimal ringing, unlike the purely Fourier-based Wiener deconvolution. Experimental results prove
that HCFRD outperforms the Wiener filter and ForWaRD algorithm in terms of both visual quality and SNR
performance.
The stochastic parallel gradient descent (SPGD) algorithm is a promising control algorithm for adaptive optics (AO), which is independent of wave-front sensor and is used to correct the wavefront distortion by optimizing the system performance metric directly. In this paper an adaptive optics experiment system with 32 control channels is set up and the static phase-distortion correction experiment is carried out to research the efficiency and performance of SPGD algorithm. The quadratic sum of intensity and the mean radius are used as the system performance metric respectively in the experiment. The experiments results demonstrate that the adaptive optics using SPGD algorithm can correct the static phase-distortion successfully. The mean radius is more sensitive to the small perturbation voltage than quadratic sum of intensity. When the mean radius is used as the system performance metric at the beginning of the correction process, and then the encircled energy is used in succession, both the convergence rate and the stability are improved.
The performance of high-resolution imaging with large optical instruments is severely limited by atmospheric
turbulence. Image deconvolution such as iterative blind deconvolution (IBD) and Richardson-Lucy (RL) deconvolution
are required. The IBD method involves the imposition of constraints such as conservation of energy, positivity, and finite
support, with known size, alternately on the image and the PSF in the spatial and Fourier domains, until convergence.
The iterative RL solution converges to the maximum likelihood solution for Poisson statistics in the data. Properties of
the RL algorithm which make it well-suited for IBD are energy conservation and the sustenance of nonnegativity. So, RL
was incorporated into the IBD framework. In this paper, an enhanced Richardson-Lucy-based iterative blind
deconvolution (ERL-IBD) algorithm is proposed to restore the blurred images due to atmospheric turbulence. The ERLIBD
incorporates dynamic PSF support estimation, bandwidth constraint of optical system, and the asymmetry factor
update. The experimental results demonstrate that the ERL-IBD algorithm works better than IBD algorithm in
deconvolution of the blurred-turbulence image.
The basic principle of the linear phase retrieval (LPR) method is introduced. It is found that in small phase condition,
the odd and even parts of phase aberration can be obtained uniquely with a simple linear calculation method. The
difference between a single measured image with aberration and the calibrated image with inherent aberration are used
to retrieve aberration phases. In this paper, the principle of LPR and its application in close-loop AO system are
introduced in vector-matrix format, which is a kind of linear calculation and is suitable for real-time calculation.
Although the LPR method is limited for small aberrations only, it is suitable to use as a wavefront sensor in close-loop
adaptive optical system. The performances of the LPR method are tested in a close-loop adaptive optics system with
PZT deformable mirror. The experiment results show that the LPR method can be performed in real time and achieve
good capabilities.
The performance of high-resolution imaging with large optical instruments is severely limited by atmospheric turbulence,
and an image deconvolution is required for reaching the diffraction limit. A new astronomical image deconvolution
algorithm is proposed, which incorporates dynamic support region and improved cost function to NAS-RIF algorithm.
The enhanced NAS-RIF (ENAS-RIF) method takes into account the noise in the image and can dynamically shrink
support region (SR) in application. In restoration process, initial SR is set to approximate counter of the true object, and
then SR automatically contracts with iteration going. The approximate counter of interested object is detected by means
of beamlet transform detecting edge. The ENAS-RIF algorithm is applied to the restorations of in-door Laser point
source and long exposure extended object images. The experimental results demonstrate that the ENAS-RIF algorithm
works better than classical NAS-RIF algorithm in deconvolution of the degraded image with low SNR and convergence
speed is faster.
The atmospheric turbulence severely limits the angular resolution of ground based telescopes. When using Adaptive
Optics (AO) compensation, the wavefront sensor data permit the estimation of the residual PSF. Yet, this estimation is
imperfect, and a deconvolution is required for reaching the diffraction limit. A joint deconvolution method based on
power spectra density (PSD) for AO image is presented. It deduces from a Bayesian framework in the context of imaging
through turbulence with adaptive optics. This method uses a noise model that accounts for photonic and detector noises.
It incorporates a positivity constraint and some a priori knowledge of the object (an estimate of its local mean and a
model for its power spectral density). Finally, it reckons with an imperfect knowledge of the point spread function (PSF)
by estimating the PSF jointly with the object under soft constraints rather than blindly. These constraints are designed to
embody our knowledge of the PSF. Deconvolution results are presented for both simulated and experimental data.
The atmospheric turbulence severely limits the angular resolution of ground based telescopes. When using Adaptive
Optics (AO) compensation, the wavefront sensor data permit the estimation of the residual PSF. Yet, this estimation is
imperfect, and a deconvolution is required for reaching the diffraction limit. It is a powerful and low-cost high-resolution
imaging technique designed to compensate for the image degradation due to atmospheric turbulence. A joint
deconvolution method based on slope measurements for AO image is presented. It deduces from a Bayesian framework
in the context of imaging through turbulence with adaptive optics. It takes into account the noise in the images and in the
Hartmann-Shack wavefront sensor measurements and the available a priori information on the object to be restored as
well as on the wave fronts. Deconvolution results are presented for experimental data.
Adaptive optical (AO) system provides a real-time compensation for atmospheric turbulence. However, the correction is often only partial, and a deconvolution is required for reaching the diffraction limit. The Richardson-Lucy (R-L) Algorithm is the technique most widely used for AO image deconvolution, but Standard R-L Algorithm (SRLA) is often puzzled by speckling phenomenon, wraparound artifact and noise problem. A Modified R-L Algorithm (MRLA) for AO image deconvolution is presented. This novel algorithm applies Magain's correct sampling approach and incorporating noise statistics to Standard R-L Algorithm. The alternant iterative method is applied to estimate PSF and object in the novel algorithm. Comparing experiments for indoor data and AO image are done with SRLA and the MRLA in this paper. Experimental results show that this novel MRLA outperforms the SRLA.
Accurate estimation of impervious surface and vegetation is a key issue in monitoring urban area and assessing urban
environments. It has been proved that the nonlinear models for spectral mixture analysis outperform the linear models in
the literature. However, the mapping functions of nonlinear models require to be predefined which are difficult to be
determined. Support vector regression (SVR) has shown success in dealing with nonlinear problem, such as estimation
and prediction. In this paper, genetic algorithm (GA) was employed to determine the optimal parameters of SVR
automatically, which were applied to SVR model. Further, a GA-SVR model with multi sets of parameters (Multi-GA-SVR)
was applied to estimate the distributions of impervious surface and vegetation. The results showed that Multi-GA-SVR
achieved a higher accuracy than GA-SVR with single set of parameters (Single-GA-SVR) and the traditional linear
mixture model (LMM), with an overall root mean square error measure (RMSE) of 0.15 for three distributions. It is
demonstrated that the proposed approach is a promising approach for estimation of impervious surface and vegetation.
A new method for microlens profile design was developed based on the analysis to the main parameters of microlens array, including micro profile formation, the numerical aperture ( NA ), the maximum sag depth for the refractive lens and the minimum zones width for the diffractive lens. With the relationships among the parameters, the microlens array in different profile can be determined effectively. The moving mask method[1] is used to realize the required profiles, an unique photolithography system have been built for implementing the mask moving exposure in both X and Y directions for the creation of microlens array. By modifying the binary moving mask, optimizing the photosensitive materials and the processing technique, the microlens profile error can be controlled in the range of 0.4µm~3µm depending on effective reliefdepth of the microlens. In our method, both diffractive and refractive microlens array with larger NA and higher fill factor can be fabricated for satisfying a plenty of purposes.
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