Modulation Transfer Function Compensation Through A Modified Wiener Filter For Spatial Image Quality Improvement.

Kebergunaan data imej yang diperolehi dari suatu sensor pengimejan amat bergantung kepada keupayaan sensor tersebut untuk meresolusikan perincian spatial ke satu tahap yang boleh diterima. The usefulness of image data acquired from an imaging sensor critically depends on the ability of the sensor to resolve spatial details to an acceptable level

Nonparametric estimation of a point-spread function in multivariate problems

The removal of blur from a signal, in the presence of noise, is readily accomplished if the blur can be described in precise mathematical terms. However, there is growing interest in problems where the extent of blur is known only approximately, for example in terms of a blur function which depends on unknown parameters that must be computed from data. More challenging still is the case where no parametric assumptions are made about the blur function. There has been a limited amount of work in this setting, but it invariably relies on iterative methods, sometimes under assumptions that are mathematically convenient but physically unrealistic (e.g., that the operator defined by the blur function has an integrable inverse). In this paper we suggest a direct, noniterative approach to nonparametric, blind restoration of a signal. Our method is based on a new, ridge-based method for deconvolution, and requires only mild restrictions on the blur function. We show that the convergence rate of the method is close to optimal, from some viewpoints, and demonstrate its practical performance by applying it to real images.Comment: Published in at http://dx.doi.org/10.1214/009053606000001442 the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical Statistics (http://www.imstat.org

F. John's stability conditions vs. A. Carasso's SECB constraint for backward parabolic problems

In order to solve backward parabolic problems F. John [{\it Comm. Pure. Appl. Math.} (1960)] introduced the two constraints "$\|u(T)\|\le M$" and $\|u(0) - g \| \le \delta$ where $u(t)$ satisfies the backward heat equation for $t\in(0,T)$ with the initial data $u(0).$ The {\it slow-evolution-from-the-continuation-boundary} (SECB) constraint has been introduced by A. Carasso in [{\it SIAM J. Numer. Anal.} (1994)] to attain continuous dependence on data for backward parabolic problems even at the continuation boundary $t=T$. The additional "SECB constraint" guarantees a significant improvement in stability up to $t=T.$ In this paper we prove that the same type of stability can be obtained by using only two constraints among the three. More precisely, we show that the a priori boundedness condition $\|u(T)\|\le M$ is redundant. This implies that the Carasso's SECB condition can be used to replace the a priori boundedness condition of F. John with an improved stability estimate. Also a new class of regularized solutions is introduced for backward parabolic problems with an SECB constraint. The new regularized solutions are optimally stable and we also provide a constructive scheme to compute. Finally numerical examples are provided.Comment: 15 pages. To appear in Inverse Problem

Polynomial computations for blind image deconvolution

This paper considers the problem of blind image deconvolution (BID) when the blur arises from a spatially invariant point spread function (PSF) H, which implies that a blurred image G is formed by the convolution of H and the exact form F of G. Since the multiplication of two bivariate polynomials is performed by convolving their coefficient matrices, the equivalence of the formation of a blurred image and the product of two bivariate polynomials implies that BID can be performed by considering F, G and H to be bivariate polynomials on which polynomial operations are performed. These operations allow the PSF to be computed, which is then deconvolved from the blurred image G, thereby obtaining a deblurred image that is a good approximation of the exact image F. Computational results show that the deblurred image obtained using polynomial computations is better than the deblurred image obtained using other methods for blind image deconvolution

Probabilistic modeling and inference for sequential space-varying blur identification

International audienceThe identification of parameters of spatially variant blurs given a clean image and its blurry noisy version is a challenging inverse problem of interest in many application fields, such as biological microscopy and astronomical imaging. In this paper, we consider a parametric model of the blur and introduce an 1D state-space model to describe the statistical dependence among the neighboring kernels. We apply a Bayesian approach to estimate the posterior distribution of the kernel parameters given the available data. Since this posterior is intractable for most realistic models, we propose to approximate it through a sequential Monte Carlo approach by processing all data in a sequential and efficient manner. Additionally, we propose a new sampling method to alleviate the particle degeneracy problem, which is present in approximate Bayesian filtering, particularly in challenging concentrated posterior distributions. The considered method allows us to process sequentially image patches at a reasonable computational and memory costs. Moreover, the probabilistic approach we adopt in this paper provides uncertainty quantification which is useful for image restoration. The practical experimental results illustrate the improved estimation performance of our novel approach, demonstrating also the benefits of exploiting the spatial structure the parametric blurs in the considered models

Spatially Separable Blind Deconvolution of Long Exposure Astronomical Imagery

In this thesis, a spatially separable blind deconvolution algorithm is demonstrated that achieves a significantly faster processing time and superior sensitivity when processing long-exposure image data of unresolvable objects from a ground-based telescope. The proposed approach takes advantage of the structure of the long exposure point spread functions radial symmetric characteristics to approximate it as a product of one dimensional horizontal and vertical intensity distributions. Objects at geosynchronous or geostationary orbit also can be well approximated as being spatially separable as they are, in general non-resolvable. The algorithms performance is measured by computing the mean-squared error compared with the true object as well as the processing time required to perform the blind deconvolution. It will be shown that images processed by the proposed technique will possess, on average, a lower mean-squared error than images that are processed through the traditional two-dimensional blind deconvolution approach. In addition, the one dimensional will be shown to perform the deconvolution significantly faster. In both cases the seeing parameter, and thus the point spread function, is treated as an unknown variable in the image reconstruction problem

On Robotic Work-Space Sensing and Control

Industrial robots are fast and accurate when working with known objects at precise locations in well-structured manufacturing environments, as done in the classical automation setting. In one sense, limited use of sensors leaves robots blind and numb, unaware of what is happening in their surroundings. Whereas equipping a system with sensors has the potential to add new functionality and increase the set of uncertainties a robot can handle, it is not as simple as that. Often it is difficult to interpret the measurements and use them to draw necessary conclusions about the state of the work space. For effective sensor-based control, it is necessary to both understand the sensor data and to know how to act on it, giving the robot perception-action capabilities. This thesis presents research on how sensors and estimation techniques can be used in robot control. The suggested methods are theoretically analyzed and evaluated with a large focus on experimental verification in real-time settings. One application class treated is the ability to react fast and accurately to events detected by vision, which is demonstrated by the realization of a ball-catching robot. A new approach is proposed for performing high-speed color-based image analysis that is robust to varying illumination conditions and motion blur. Furthermore, a method for object tracking is presented along with a novel way of Kalman-filter initialization that can handle initial-state estimates with infinite variance. A second application class treated is robotic assembly using force control. A study of two assembly scenarios is presented, investigating the possibility of using force-controlled assembly in industrial robotics. Two new approaches for robotic contact-force estimation without any force sensor are presented and validated in assembly operations. The treated topics represent some of the challenges in sensor-based robot control, and it is demonstrated how they can be used to extend the functionality of industrial robots

Restauration de défauts de S-VHS

Le VHS est encore le format de bande magnétique le plus populaire dans le domaine de l'enregistrement de vidéo pour les consommateurs. En raison de la limitation de la résolution horizontale, une version améliorée du format VHS a été introduite: le format S-VHS. Bien que le nouveau format permette de reproduire l'image avec une bonne qualité, il y a encore des défauts: (1) L'image semble brouillée, et les détails manquent de netteté. (2) Les couleurs sont dégradées et manquent d'éclat. Ce travail propose deux méthodes de restauration de défauts de S-VHS. La première combine les techniques de débrouillage avec les techniques de rehaussement. Dans cette méthode, la luminance et la chrominance sont traitées séparément. La deuxième approche proposée utilise une structure de réseaux de neurones en cascade et permet d'utiliser l'information dans la luminance traitée afin de mieux restaurer la chrominance. Les résultats d'expériences ont montré que les deux méthodes proposées sont capables de donner des images plus nettes et plus éclatantes. En plus, grâce à la simplicité et la non-itération, il est possible de les faire fonctionner en temps-reél