Power Aware Routing in Mobile Ad Hoc Networks

Proceedings of world academy of science, engineering and technology, vol 8

Speckle noise which occurs due to the coherent imag ing system is the best known problem of SAR images and in turn, affects classification, change detection, biomass estimatio n and interpretation results. Several adaptive filt ering methods have been documented to deal with this issue, such as Kuan, L ee, MMSE and Frost filters. These filters do not co nsider the level of homogeneity in the intensity of the pixels. For thi s reason, they degrade the spatial resolution of im age and smooth details, while significantly decreasing the speckle noise level. T here are other filters such as Enhanced Lee and Gam ma Map that utilize the level of homogeneity, but they cannot adequately suppress speckle noise. Moreover, pixels whose coefficients of variation are near to maximum and minimum threshold values are not correctly filtered using these filters. In addition to th ese weaknesses, pixels surrounding a point scatterer are also treated as p oint scatterers due to shortcoming of the method of evaluating the coefficient of variation for differentiating between them and the point scatterer. We have developed a new method based on the homogeneity level for speckle noise suppression and simultaneously ed ge and feature preservation. Also, an algorithm has been proposed based on local statistical information to filter the pixels surrou nding point scatterers. The results show an improve ment in speckle reduction and texture preservation as well as reduction in the nu mber of unfiltered pixels.

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Image texture preservation in speckle noise suppression

Feature selection methods select a subset of attributes (features) of a dataset and it is done based on a defined measure, eliminating the redundant and irrelevant ones. When a feature selection method is applied in a dataset, we aim to improve the quality of the dataset representation. For ensemble systems, feature selection techniques can supply different feature subsets for the individual components, reducing the redundancy that can exist among the features of an input pattern and to increase the diversity level of these systems. This paper proposes the application of three well-known optimization techniques (particle swarm optimization, ant-colony optimization and genetic algorithms), in both mono and bi-objective versions, to choose subsets of features for the individual components of ensembles. The feature selection process was based on two filter-based evaluation criteria that tried to capture the idea of diversity of individual classifiers and group diversity of an ensemble system. In this case, these optimization techniques try to maximize these diversities measures, either individually (mono-objective) or together (bi-objective). An empirical analysis was performed, where all ensemble systems were applied to 11 datasets and we compared both mono and bi-objective versions among each other and with a random subset procedure. Based on the empirical analysis, we will observe that PSO with a bi-objective function will be the most promising direction, when selecting attributes for individual components of ensemble systems.

Filter-based optimization techniques for selection of feature subsets in ensemble systems

Image interpolation is the problem of increasing the resolution of a given image. An important aspect of interpolation is accurate estimation of edge orientations. This work introduces contour stencils, a new method for estimating the image contours based on total variation along curves. Contour stencils are able to distinguish lines of different orientations, curves, corners, and other geometric features with a computationally efficient formula. Contour stencils are applied in designing an edge directed color interpolation method. The method incorporates an efficient approximation of deconvolution. Although most interpolation methods that involve deconvolution require either solving a large linear system or running many iterations, this method has linear complexity in the number of pixels and can be computed in one or a small number of passes through the image. Comparisons show that the proposed interpolation is competitive with existing methods.

Contour Stencils: Total Variation along Curves for Adaptive Image Interpolation

World has entered in 21 century. The technology of computer graphics and digital cameras is prevalent. High resolution display and printer are available. Therefore high resolution images are needed in order to produce high quality display images and high quality prints. However, since high resolution images are not usually provided, there is a need to magnify the original images. One common difficulty in the previous magnification techniques is that of preserving details, i.e. edges and at the same time smoothing the data for not introducing the spurious artefacts. A definitive solution to this is still an open issue. In this paper an image magnification using adaptive interpolation by pixel level data-dependent geometrical shapes is proposed that tries to take into account information about the edges (sharp luminance variations) and smoothness of the image. It calculate threshold, classify interpolation region in the form of geometrical shapes and then assign suitable values inside interpolation region to the undefined pixels while preserving the sharp luminance variations and smoothness at the same time. The results of proposed technique has been compared qualitatively and quantitatively with five other techniques. In which the qualitative results show that the proposed method beats completely the Nearest Neighbouring (NN), bilinear(BL) and bicubic(BC) interpolation. The quantitative results are competitive and consistent with NN, BL, BC and others. Keywords—Adaptive, digital image processing, image magnification, interpolation, geometrical shapes, qualitative & quantitative analysis.

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Image Magnification Using Adaptive Interpolationby Pixel Level Data-Dependent Geometrical Shapes

In this paper we introduced new wavelet based algorithm for speckle reduction of synthetic aperture radar images, which uses combination of undecimated wavelet transformation, wiener filter (which is an adaptive filter) and mean filter. Further more instead of using existing thresholding techniques such as sure shrinkage, Bayesian shrinkage, universal thresholding, normal thresholding, visu thresholding, soft and hard thresholding, we use brute force thresholding, which iteratively run the whole algorithm for each possible candidate value of threshold and saves each result in array and finally selects the value for threshold that gives best possible results. That is why it is slow as compared to existing thresholding techniques but gives best results under the given algorithm for speckle reduction. Keywords—Brute force thresholding, Directional smoothing, Direction dependent mask, undecimated wavelet transformation.

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Wavelet-Based Despeckling of Synthetic Aperture Radar Images Using Adaptive and Mean Filters

This paper presents a novel method for data hiding based on neighborhood pixels information to calculate the number of bits that can be used for substitution and modified Least Significant Bits technique for data embedding. The modified solution is independent of the nature of the data to be hidden and gives correct results along with un-noticeable image degradation. The technique, to find the number of bits that can be used for data hiding, uses the green component of the image as it is less sensitive to human eye and thus it is totally impossible for human eye to predict whether the image is encrypted or not. The application further encrypts the data using a custom designed algorithm before embedding bits into image for further security. The overall process consists of three main modules namely embedding, encryption and extraction.cm. Keywords—Data Hiding, Image Processing, Information Security, Stagonography.

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A Robust Method for Encrypted Data Hiding Technique Based on Neighborhood Pixels Information

This paper compares and documents the work of an optimized fingerprint core point determination algorithm. This work focuses to present an efficient and precise way for the extraction of core point. Core Point is detected using least mean square algorithm. Orientation is figured out via a novel efficient technique. This technique is better in terms of time and accuracy as compared to other two techniques mentioned in this paper. Poin'care index is then used to find out the location of core point. Found point may be slightly in error so further refinement is performed. For further tuning, a small window of specific size (w x w) is cropped around that point. This area is now tuned via PC after making some enhancements to it. The results obtained are very good and are produced efficiently. Proposed algorithm provides the hybrid platform where refinement can be done via any technique.

Optimization of Core Point Detection in Fingerprints

This paper introduces the effective speckle reduction of synthetic aperture radar (SAR) images using inner product spaces in undecimated wavelet domain. There are two major areas in projection onto span algorithm where improvement can be made. First is the use of undecimated wavelet transformation instead of discrete wavelet transformation. And second area is the use of smoothing filter namely directional smoothing filter which is an additional step. Proposed method does not need any noise estimation and thresholding technique. More over proposed method gives good results on both single polarimetric and fully polarimetric SAR images. Keywords—Directional Smoothing, Inner product, Length of vector, Undecimated wavelet transformation.

Naveed Sarfraz Khattak

Papers

Image Magnification Using Adaptive Interpolationby Pixel Level Data-Dependent Geometrical Shapes

Wavelet-Based Despeckling of Synthetic Aperture Radar Images Using Adaptive and Mean Filters

A Robust Method for Encrypted Data Hiding Technique Based on Neighborhood Pixels Information

Optimization of Core Point Detection in Fingerprints

Despeckling of Synthetic Aperture Radar Images Using Inner Product Spaces in Undecimated Wavelet Domain