Wave-front sensing from defocused images by use of wave-front slopes

We describe a novel technique for deriving wave-front aberrations from two defocused intensity measurements. The intensity defines a probability density function, and the method is based on the evolution of the cumulative density function of the intensity with light propagation. In one dimension, the problem is easily solved with a histogram specification procedure, with a linear relationship between the wave-front slope and the difference in the abscissas of the histograms. In two dimensions, the method requires use of a Radon transform. Simulation results demonstrate that good reconstructions can be attained down to 100 photons in each detector. In addition, the method is insensitive to scintillation at the aperture.     

Multiwindowed defocused electronic speckle photographic system for tilt measurement

Defocused speckle photography has long been used to measure rotations of rough surfaces. By addition of a suitably perforated mask, some measurement properties, such as range and lateral resolution, may be changed at will. In particular, the maximum measurable tilt can be significantly increased, although at the expense of poorer lateral resolution. Advantages of this compared with previously described techniques include independent tuning of speckle size and optical system aperture and greater adaptability to various measuring needs. The benefits and disadvantages of the new and old techniques are thoroughly compared.     

Discrete cosine transform domain restoration of defocused images

In discrete-cosine-transform-based (DCT-based) compressions such as JPEG it is a common practice to use the same quantization matrix for both encoding and decoding. However, this need not be the case, and the flexibility of designing different matrices for encoding and decoding allows us to perform image restoration in the DCT domain. This is especially useful when we have severe limitations on the computational power, for instance, with in-camera image manipulation for programmable digital cameras. We provide an algorithm that compensates partially for a defocus error in image acquisition, and experimental results show that the restored image is closer to the in-focus image than is the defocused image.     

Quantitative analysis of error bounds in the recovery of depth from defocused images

Depth from defocus involves estimating the relative blur between a pair of defocused images of a scene captured with different lens settings. When a priori information about the scene is available, it is possible to estimate the depth even from a single image. However, experimental studies indicate that the depth estimate improves with multiple observations. We provide a mathematical underpinning to this evidence by deriving and comparing the theoretical bounds for the error in the estimate of blur corresponding to the case of a single image and for a pair of defocused images. A new theorem is proposed that proves that the Cramér-Rao bound on the variance of the error in the estimate of blur decreases with an increase in the number of observations. The difference in the bounds turns out to be a function of the relative blurring between the observations. Hence one can indeed get better estimates of depth from multiple defocused images compared with those using only a single image, provided that these images are differently blurred. Results on synthetic as well as real data are given to further validate the claim.     

Digital restoration of defocused images in the wavelet domain

Many cameras nowadays are equipped with an autofocus mechanism that attempts to take pictures at the best possible focus. However, there are situations in which the pictures are still out of focus, such as when the photographer has mistakenly focused at a wrong position or when the focusing region consists of objects of different depths and therefore confuses the autofocus system. With a digital camera, we can attempt to use digital image-restoration techniques to bring the pictures back into focus. We design an algorithm that restores the image by digitally enhancing the corresponding frequency bands. We employ the restoration in the wavelet domain so that this restoration scheme can be compliant with JPEG 2000, which is positioned to succeed JPEG as the next image-compression standard and has the potential to be widely adopted by the digital photography industry owing to its many advanced features.     

Correlation postprocessing-based method for the detection of defocused images

We present a method to detect patterns in defocused scenes by means of a joint transform correlator. We describe analytically the correlation plane, and we also introduce an original procedure to recognize the target by postprocessing the correlation plane. The performance of the methodology when the defocused images are corrupted by additive noise is also considered.     

Rotational-diversity phase estimation from differential-interference-contrast microscopy images

An iterative phase-estimation method for the calculation of a specimen's phase function or optical-path-length (OPL) distribution from differential-interference-contrast (DIC) microscopy images is presented. The method minimizes the least-squares discrepancy measure by use of the conjugate-gradient technique to estimate the phase function from multiple DIC images acquired at different specimen rotations. The estimate is regularized with a quadratic smoothness penalty. Results from testing the method with simulations and measured DIC images show improvement in the estimated phase when at least two rotationally diverse DIC images instead of a single DIC image are used for the estimation. The OPL of a cell that is estimated from two DIC images was found to be much more reliable than the OPL computed from single DIC images (which had a coefficient of variation equal to 15.8%).     

Efficient disparity estimation from stereo images using hybrid-guided image filter

Stereo vision process involves capturing the pictures from a camera of the same scene from at least two different locations and calculating the three-dimensional information. Conventionally, these two versions of snapshots are called left and right views which yield the depth information of an object upon relative comparison of its location in two views. Although the stereo image and its applications are becoming increasingly prevalent, there has been very limited research on disparity estimation from stereo images. Most of the existing techniques suffer from the gradient reversal artefacts issue. Therefore, to handle this issue, we have proposed a hybrid-guided image filter for improving the disparity estimation from stereo images. The hybrid filter utilizes the features of guided image filter and Bayesian non-local means with edge aware constraint. Maximum likelihood and local area homogeneity analysis are used to generate the guidance image for the proposed filter. To enhance the quality of disparity estimation from stereo images, segmentation is also done using the modified mean shift technique. Experimental results show that the proposed technique can efficiently estimate the depth maps over the available techniques. One-way ANOVA analysis on experimental results validates that the hybrid filter-based stereo matching outperforms consistently over the state-of-art approaches.     

Phase object data obtained from defocused laser speckle displacement

An optical technique that is based on defocused digital speckle photography is proposed for the evaluation of phase objects. Phase objects are different kinds of transparent or semi-transparent media that allow light to be transmitted. A phase object inserted in a laser speckle field introduces speckle displacement, from which information about the object may be extracted. It is shown that one may use speckle displacements to determine both the phase gradients and the positions of phase objects. As an illustration the positions and focal lengths of two weak lenses have been derived from defocused laser speckle displacement.     

Turbulence strength estimation from an arbitrary set of atmospherically degraded images

In remote sensing, atmospheric turbulence and aerosols usually limit the image quality. For many practical cases, turbulence is shown to be dominant, especially for horizontal close-to-earth imaging in hot environments. In a horizontal long-range imaging, it is usually impractical to calculate path-averaged refractive index structure constant C(2)(n) (which characterizes the turbulence strength) with conventional equipment. We propose a method for estimating C(2)(n) from the available atmospherically degraded video sequence by calculating temporal intensity fluctuations in spatially high variance areas. Experimental comparison with C(2)(n) measurements using a scintillometer shows reliable estimation results.     

Unsupervised estimation of speckle noise in radar images

Speckle in radar images has the characteristic of a multiplicative noise. In this article, two unsupervised methods are introduced to estimate the speckle noise statistics using the mean and the standard deviation of small image blocks (4 × 4, or 6 × 6 pixels). Since most radar images contain many small homogeneous areas, a scatter plot of the standard deviation versus the mean can reveal the characteristic of the noise. The blocks from inhomogeneous areas have higher values for the standard deviation, and they are scattered above the main cluster. They are considered as outliers, and should be excluded in the statistical estimation. These two methods are designed for obtaining a linear fit in the scatter plot by ignoring outliers. Several synthetic aperture radar(SAR) images are used for illustration.     

Automatic crack tip detection and stress intensity factors estimation of curved cracks from digital images

With the development of full‐field measurement techniques, it has been possible to analyze crack propagation experimentally with an increasing level of robustness. However, the analysis of curved cracks is made difficult and almost unexplored because the possible analysis domain size decreases with crack curvature, leading to an increasing uncertainty level. This paper proposes a digital image correlation technique, augmented by an elastic regularization, combining finite element kinematics on an adapted mesh and a truncated Williams' expansion. Through the analysis of two examples, the proposed technique is shown to be able to address the experimental problems of crack tip detection and stress intensity factors estimation along a curved crack path. Copyright © 2015 John Wiley & Sons, Ltd.     

Incorporating higher-order modal measurements in tilt estimation: natural and laser guide star applications

Tilt compensation performance is generally suboptimal when phase measurements from natural or laser guide stars are used as the conjugate phase in an adaptive optics system. Optimal compensation is obtained when the conjugate-phase coefficients are estimated from beacon measurements, given knowledge of the correlation between the on-axis object phase and the beacon measurements. We apply optimal compensation theory to tilt correction for the case of an off-axis beacon. Because off-axis higher-order modes are correlated with the on-axis tilt components, a performance gain can be realized when the tilt estimator includes higher-order modal measurements. For natural guide star compensation, it is shown that equivalent tilt compensation can be achieved at beacon offsets that are three times larger when higher-order modes through Zernike 15 are used in the tilt estimator. For a laser guide star, although tilt information cannot be measured directly because of beam reciprocity, off-axis higher-order modal measurements can be used to estimate tilt components, leading to a maximum Strehl ratio of approximately 0.3 for the relative aperture diameter D/r(0) = 4 and the relative turbulence outer scale L(0)/D = 10.     

Using independent component analysis for material estimation in hyperspectral images

We develop a method for automated material estimation in hyperspectral images. The method models a hyperspectral pixel as a linear mixture of unknown materials. The method is particularly useful for applications in which material regions in a scene are smaller than one pixel. In contrast to many material estimation methods, the new method uses the statistics of large numbers of pixels rather than attempting to identify a small number of the purest pixels. The method is based on maximizing the independence of material abundances at each pixel. We show how independent component analysis algorithms can be adapted for use with this problem. We demonstrate properties of the method by application to airborne hyperspectral data.     

一种新的人脸图像本征维数估计方法

利用拓扑理论中的单形定义,本文提出了一种针对人脸图像本征维数的估计方法.首先给出一个简单的几何模型,说明在不同的姿态和光照条件下人脸图像可看成一个弯曲流形;然后把人脸流形模型近似为一个单纯复形,获得相应的单形数目;最后,利用单纯复形中单形的最大维数是单纯复形的维数的性质,从而估计出人脸图像的本征维数.实验结果表明,本文方法跟经典方法,如分形方法、熵估计方法及k-近邻图方法等相比较,在人脸图像本征维数估计方面是有效的和准确的.     

Parametric estimation of the square degree of polarization from two intensity images degraded by fully developed speckle noise

Active polarimetric imagery systems allow one to reveal polarimetric characteristics of the scene. Among them, the degree of polarization allows one to have information about the polarizing nature of an imaged object. Its estimation is standardly done from four images of the scene. Reducing this number of images can be of great interest for industrial applications, allowing in particular reduction of cost in terms of money and acquisition time. We propose a parametric method to estimate the square degree of polarization from only two measurements when coherent illumination is considered and when the images are corrupted with fully developed speckle, and we characterize the performances of the estimation.     

利用Java实现对云台和镜头的远程控制

结合我国实际情况和未来的发展趋势,开发出基于Java的视频监控系统,实现对云台和镜头的远程控制。系统包括RS-232/RS-485的转换,解码器和云台、镜头以及它们与计算机串口的硬件连接,通过Pelco-D协议,采用Java的扩展类库comm.jar库进行串口通信。客户端通过套接字对服务器端的云台和镜头进行控制,对于同时来自服务器端和多个远程客户端对云台和镜头的控制所产生的冲突提出了解决方法。系统具有跨平台性。     

Phase retrieval from speckle images

In ground-based astronomy, the inverse problem of phase retrieval from speckle images is a means to calibrate static aberrations for correction by active optics. It can also be used to sense turbulent wavefronts. However, the number of local minima drastically increases with the turbulence strength, mainly because of phase wrapping ambiguities. Multifocal phase diversity has been considered to overcome some ambiguities of the phase retrieval problem. We propose an effective algorithm for phase retrieval from a single focused image. Our algorithm makes use of a global optimization strategy and an automatically tuned smoothness prior to overcome local minima and phase degeneracies. We push the limit of D/r(0)=4 achieved by Irwan and Lane [J. Opt. Soc. Am. A.15, 2302 (1998)] up to D/r(0)=11, which is a major improvement owing to the drastic increase in the problem complexity. We estimate the performances of our approach from consistent simulations for different turbulence strengths and noise levels (down to 1500 photons per image). We also investigate the benefit of temporal correlation.     

Extracting useful information from images

The paper presents an overview of methods for extracting useful information from digital images. It covers various approaches that utilized different properties of images, like intensity distribution, spatial frequency content and several others. A few case studies including isotropic and heterogeneous, congruent and non-congruent images are used to illustrate how the described methods work and to compare some of them.     

Matching radar and satellite images for ship trajectory estimation using the Hausdorff distance

A novel technique to obtain position, velocity and radar biases estimates of a ship is described by matching ship-borne radar images to geo-referenced satellite images. The matching is performed through the minimisation of the averaged partial Hausdorff distances between data points in each image. The minimisation rapidly yields robust geographical latitude and longitude position measurements, as well as ship heading and radar biases. The accuracy of the measurements is improved by feeding them into a Kalman filter that also allows estimates of the ship's velocity to be obtained. The method provides an alternative effective position sensor for GPS denied environments, which may also be employed for automatic radar calibration of bearing and range biases or for indoor autonomous mobile robot navigation