Poisson inverse problems by the Plug-and-Play scheme

The easy-to-compute Anscombe transform offers a conversion of a Poisson random variable into a variance stabilized Gaussian one, thus becoming handy in various Poisson-noisy inverse problems. Solution to such problems can be done by applying this transform, then invoking a high-performance Gaussian-noise-oriented restoration algorithm, and finally using an inverse transform. This process works well for high-SNR images, but when the noise level is high, it loses much of its effectiveness. This work suggests a novel method for coupling Gaussian denoising algorithms to Poisson noisy inverse problems. This approach is based on a general approach termed “Plug-and-Play-Prior”. Deploying this to Poisson inverse-problems leads to an iterative scheme that repeats an easy treatable convex programming task, followed by a powerful Gaussian denoising This method, like the Anscombe transform, enables to plug Gaussian denoising algorithms for the Poisson-oriented problem, and yet, it is effective for all SNR ranges.     

A statistical multiscale framework for Poisson inverse problems

This paper describes a statistical multiscale modeling and analysis framework for linear inverse problems involving Poisson data. The framework itself is founded upon a multiscale analysis associated with recursive partitioning of the underlying intensity, a corresponding multiscale factorization of the likelihood (induced by this analysis), and a choice of prior probability distribution made to match this factorization by modeling the “splits” in the underlying partition. The class of priors used here has the interesting feature that the “noninformative” member yields the traditional maximum-likelihood solution; other choices are made to reflect prior belief as to the smoothness of the unknown intensity. Adopting the expectation-maximization (EM) algorithm for use in computing the maximum a posteriori (MAP) estimate corresponding to our model, we find that our model permits remarkably simple, closed-form expressions for the EM update equations. The behavior of our EM algorithm is examined, and it is shown that convergence to the global MAP estimate can be guaranteed. Applications in emission computed tomography and astronomical energy spectral analysis demonstrate the potential of the new approach     

Remote sensing image registration via active contour model

Image registration is the process by which we determine a transformation that provides the most accurate match between two images. The search for the matching transformation can be automated with the use of a suitable metric, but it can be very time-consuming and tedious. In this paper, we introduce a registration algorithm that combines active contour segmentation together with mutual information. Our approach starts with a segmentation procedure. It is formed by a novel geometric active contour, which incorporates edge knowledge, namely Edgeflow, into active contour model. Two edgemap images filled with closed contours are obtained. After ruling out mismatched curves, we use mutual information (MI) as a similarity measure to register two edgemap images. Experimental results are provided to illustrate the performance of the proposed registration algorithm using both synthetic and multisensor images. Quantitative error analysis is also provided and several images are shown for subjective evaluation.     

Saturation-based adaptive inverse gradient interpolation for Bayerpattern images

A saturation-based adaptive gradient interpolation algorithm is developed for Bayer pattern images obtained in single-sensor digital cameras. The proposed algorithm uses the concept of pseudo-saturation for 2×2 blocks of the image. The algorithm adapts the scheme of interpolation based on the classification of the pseudo-saturation of Bayer pattern images. Compared with other algorithms, the proposed method preserves fine details, reduces false colours, and distortions around edges and sharp features of the colour image reconstructed from different kinds of Bayer pattern images     

Conjugate gradient method applied to inverse scattering problem

A new reconstruction algorithm for diffraction tomography is presented. The algorithm is based on the minimization of a functional which is defined as the norm of the discrepancy between the measured scattering amplitude and the calculated one for an estimated object function. By using the conjugate gradient method to minimize the functional, one can derive an iterative formula for getting the object function. Numerical results for some two-dimensional scatterers show that the algorithm is very effective in reconstructing refractive index distributions to which the first-order Born approximation can not be applied. In addition, the number of iterations is reduced by using a priori information about the outer boundary of the objects. Furthermore, the method is not so sensitive to the presence of noise in the scattered field data     

Automatic body segmentation with graph cut and self-adaptive initialization level set (SAILS)

In this paper, we propose an automatic human body segmentation system which mainly consists of human body detection and object segmentation. Firstly, an automatic human body detector is designed to provide hard constraints on the object and background for segmentation. And a coarse-to-fine segmentation strategy is employed to deal with the situation of partly detected object. Secondly, background contrast removal (BCR) and self-adaptive initialization level set (SAILS) are proposed to solve the tough segmentation problems of the high contrast at object boundary and/or similar colors existing in the object and background. Finally, an object updating scheme is proposed to detect and segment new object when it appears in the scene. Experimental results demonstrate that our body segmentation system works very well in the live video and standard sequences with complex background.     

Excursions of adaptive algorithms via the Poisson clumpingheuristic

The authors detail the application of the Poisson clumping heuristic (PCH) to the least mean square (LMS) adaptive algorithm and its signed variants. Under certain conditions on the input and disturbance statistics, the parameter estimate errors form a Markov process. The PCH asserts that large excursions of the parameter estimates occur in clumps, and that these clumps are distributed in a Poisson manner with parameter λ_b, where b is the maximum parameter error. Expressions are derived for each of the four algorithms in the scalar case, which allows calculation of λ _b in a relatively straightforward manner. These values are compared to simulations of the algorithms. Given that the results are asymptotic in b, the close agreement between simulated and theoretical values is striking, even for very modest b. The four algorithms are then compared in terms of λ_b. Some observations are made regarding the relative performance of the four variants. No single LMS variant always outperforms the others. Suggestions are made as to how this technique might be applied in the vector case, and a crucial monotonicity property is verified     

Automatic gradient threshold determination for edge detection

We describe a method to automatically find gradient thresholds to separate edge from nonedge pixels. A statistical model that is the weighted sum of two gamma densities corresponding to edge and nonedge pixels is used to identify a threshold. Results closely match human perceptual thresholds even under low signal-to-noise ratio (SNR) levels.     

Computation of the inverse Hessian matrix using conjugate gradient methods

A method is presented to compute the inverse of the Hessian matrix in an optimization problem using the conjugate gradient algorithm of Hestenes and Stiefel [1]. It is shown how this may be used to refine the solution by a Newton-Raphson iteration for both finite and infinite dimensional optimization problems.     

Intravital leukocyte detection using the gradient inverse coefficient of variation

The problem of identifying and counting rolling leukocytes within intravital microscopy is of both theoretical and practical interest. Currently, methods exist for tracking rolling leukocytes in vivo, but these methods rely on manual detection of the cells. In this paper we propose a technique for accurately detecting rolling leukocytes based on Bayesian classification. The classification depends on a feature score, the gradient inverse coefficient of variation (GICOV), which serves to discriminate rolling leukocytes from a cluttered environment. The leukocyte detection process consists of three sequential steps: the first step utilizes an ellipse matching algorithm to coarsely identify the leukocytes by finding the ellipses with a locally maximal GICOV. In the second step, starting from each of the ellipses found in the first step, a B-spline snake is evolved to refine the leukocytes boundaries by maximizing the associated GICOV score. The third and final step retains only the extracted contours that have a GICOV score above the analytically determined threshold. Experimental results using 327 rolling leukocytes were compared to those of human experts and currently used methods. The proposed GICOV method achieves 78.6% leukocyte detection accuracy with 13.1% false alarm rate.     

改进的基于泊松模型的极大似然恢复算法

从高速流场扰动的失真图像中恢复出满足制导系统所需精度的目标图像是需要研究的课题.Holmes的基于泊松模型的极大似然校正算法存在平凡解的问题,针对此问题,提出在原算法的极大似然概率估计函数中增加适当的修正项来约束迭代校正算法向正确的方向收敛.讨论了修正项中参数的优化问题,提出按照使校正后图像的熵最小的优化准则来选取合适的修正参数.最后使用了实际风洞模拟实验中获得的气动光学效应降晰图像来测试改进算法的校正效果,同时给出原算法的校正结果以便比较.测试结果表明,改进算法有效消除了原算法的平凡解问题,校正效果较原算法有较为明显的提高.     

Automatic aircraft landing using interferometric inverse syntheticaperture radar imaging

This paper presents an interferometric processing of an aircraft's monostatic and bistatic inverse synthetic aperture radar (ISAR) signatures for automatic landing. The aircraft's squint angle in this ISAR imaging problem is near 90 degrees . We show that this extreme squint angle does not pose any problem for the ISAR Fourier-based (wavefront) reconstruction algorithm. In fact, the aircraft can be imaged accurately, and without any erroneous shifts in the cross-range domain, within the imposed theoretical resolution. Moreover, the algorithm is accurate enough such that one can utilize the phase of the ISAR monostatic and bistatic measurements for interferometric processing. The resultant interferometric ISAR image is used to detect undesirable rotations in the aircraft's orientation.     

Noise relations of inverse active networks and complementary networks

A direct relation is derived to calculate the noise of an inverse active network from the noise of its pair. A similar relation for complementary networks is also derived. Applications of these relations are given.     

Automatic target recognition organized via jump-diffusionalgorithms

Proposes a framework for simultaneous detection, tracking, and recognition of objects via data fused from multiple sensors. Complex dynamic scenes are represented via the concatenation of simple rigid templates. The variability of the infinity of pose is accommodated via the actions of matrix Lie groups extending the templates to individual instances. The variability of target number and target identity is accommodated via the representation of scenes as unions of templates of varying types, with the associated group transformations of varying dimension. We focus on recognition in the air-to-ground and ground-to-air scenarios. The remote sensing data is organized around both the coarse scale associated with detection as provided by tracking and range radars, along with the fine scale associated with pose and identity supported by high-resolution optical, forward looking infrared and delay-Doppler radar imagers. A Bayesian approach is adopted in which prior distributions on target scenarios are constructed via dynamical models of the targets of interest. These are combined with physics-based sensor models which define conditional likelihoods for the coarse/fine scale sensor data given the underlying scene. Inference via the Bayes posterior is organized around a random sampling algorithm based on jump-diffusion processes. New objects are detected and object identities are recognized through discrete jump moves through parameter space, the algorithm exploring scenes of varying complexity as it proceeds. Between jumps, the scale and rotation group transformations are generated via continuous diffusions in order to smoothly deform templates into individual instances of objects.     

Image segmentation and analysis via multiscale gradient watershedhierarchies

Multiscale image analysis has been used successfully in a number of applications to classify image features according to their relative scales. As a consequence, much has been learned about the scale-space behavior of intensity extrema, edges, intensity ridges, and grey-level blobs. We investigate the multiscale behavior of gradient watershed regions. These regions are defined in terms of the gradient properties of the gradient magnitude of the original image. Boundaries of gradient watershed regions correspond to the edges of objects in an image. Multiscale analysis of intensity minima in the gradient magnitude image provides a mechanism for imposing a scale-based hierarchy on the watersheds associated with these minima. This hierarchy can be used to label watershed boundaries according to their scale. This provides valuable insight into the multiscale properties of edges in an image without following these curves through scale-space. In addition, the gradient watershed region hierarchy can be used for automatic or interactive image segmentation. By selecting subtrees of the region hierarchy, visually sensible objects in an image can be easily constructed.     

Automatic analysis of two-port active microwave network

A new six-port ANA technique and its application in analysing an active microwave network are discussed. The reported isolated dual-six-port ANA (IDSPNA) provides a simple and powerful means to determine any two-port network scattering matrix. Computer simulation and experimental results for MIC amplifiers are presented.     

A cluster-modified Poisson model for estimating defect density andyield

A simple modification of the Poisson model that accounts for defect clustering when estimating the defect density and yield of a future product is described. The Poisson yield equation is easy to use and interpret, and the composite model can be extended to a layered model without the difficulties associated with, for example, the negative binomial model. The advantage of the method is that the Poisson model, to which all yield models reduce as yields increase, can continue to be used with less effort than, more convenience than, and at least as good accuracy as other more complicated models     

Automatic detection of phishing pages with event-based request processing,deep-hybrid feature extraction and light gradient boosted machine model

Telecommunication Systems - Cyber attackers target unconscious users with phishing methods is a serious threat to cyber security. It is important to quickly detect benign web pages according to...