In this work, we are obviously interested in a general solution for the calculation of the image of a single bar in partially coherent illumination. The solution is based on the theory of Hopkins for the formation of images in optical instruments in which it was shown that for all practical cases, the illumination of the object may be considered as due to a self – luminous source placed at the exit pupil of the condenser , and the diffraction integral describing the intensity distribution in the image of a single bar – as an object with half – width (U0 = 8 ) and circular aperture geometry is viewed , which by suitable choice of the coherence parameters (S=0.25,1.0.4.0) can be fitted to the observed distribution in various types of microscope , the aberration were restricted to defocusing and coma upto third – order , the method of integration was Gauss quadrature: The necessary set of integration depends , of course , on the amount of present aberrations and had to be chosen (20) points of Gauss which decrease the computation time to few seconds: The aberration free systems corresponding to the paraxial receiving plane (W20= 0.0) is especially interesting as it predicts diffraction pattern shape. The influence of defocusing is very pronounced and relatively distorts the object , the influence of the off – axis aberration (third – order coma ), in which it was shown that for the high peaks in the images are most noticeable in the region of almost perfect coherence (S=0.25). As (S) is increased from (0.25) to (1.0) there is a pronounced redistribution of intensity, with peaks moving from one side of the image to the other. Calculations were also performed for systems having spherical aberration, but the results are qualitatively similar to an aberration – free defocused system and are omitted, so we will not present any numerical results. A computer program was written in FORTRAN 77 which solved the modified intensity distribution of Hopkins for(U´) dimensionless distance. The advantage of that additional work on this class of problems to investigate the development of more efficient numerical methods, also the reduction in computation time to few seconds for data runs for individual curves of intensity.
Groupwise non-rigid image alignment is a difficult non-linear optimization problem involving many parameters and often large datasets. Previous methods have explored various metrics and optimization strategies. Good results have been previously achieved with simple metrics, requiring complex optimization, often with many unintuitive parameters that require careful tuning for each dataset. In this chapter, the problem is restructured to use a simpler, iterative optimization algorithm, with very few free parameters. The warps are refined using an iterative Levenberg-Marquardt minimization to the mean, based on updating the locations of a small number of points and incorporating a stiffness constraint. This optimization approach is eff
... Show MoreHM Al-Dabbas, RA Azeez, AE Ali, Iraqi Journal of Science, 2023
Due to the vast using of digital images and the fast evolution in computer science and especially the using of images in the social network.This lead to focus on securing these images and protect it against attackers, many techniques are proposed to achieve this goal. In this paper we proposed a new chaotic method to enhance AES (Advanced Encryption Standards) by eliminating Mix-Columns transformation to reduce time consuming and using palmprint biometric and Lorenz chaotic system to enhance authentication and security of the image, by using chaotic system that adds more sensitivity to the encryption system and authentication for the system.
One of the significant stages in computer vision is image segmentation which is fundamental for different applications, for example, robot control and military target recognition, as well as image analysis of remote sensing applications. Studies have dealt with the process of improving the classification of all types of data, whether text or audio or images, one of the latest studies in which researchers have worked to build a simple, effective, and high-accuracy model capable of classifying emotions from speech data, while several studies dealt with improving textual grouping. In this study, we seek to improve the classification of image division using a novel approach depending on two methods used to segment the images. The first
... Show MoreThis paper presents the matrix completion problem for image denoising. Three problems based on matrix norm are performing: Spectral norm minimization problem (SNP), Nuclear norm minimization problem (NNP), and Weighted nuclear norm minimization problem (WNNP). In general, images representing by a matrix this matrix contains the information of the image, some information is irrelevant or unfavorable, so to overcome this unwanted information in the image matrix, information completion is used to comperes the matrix and remove this unwanted information. The unwanted information is handled by defining {0,1}-operator under some threshold. Applying this operator on a given ma
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