Current Issue : January - March Volume : 2015 Issue Number : 1 Articles : 4 Articles
In this paper, we study the potential of the quaternionic wavelet transform for the analysis and processing of\nmultispectral images with strong structural information. This new representation gives a very good division of the\ncoefficients in terms of magnitude and three-phase angles and generalizes better the concept of analytic signal to\nimage. Furthermore, it retains the property of shift invariant and directivity. We show an application of this transform in\nsatellite image denoising. The proposed approach relies on the adaptation of thresholding procedures based on the\ndependency between magnitude quaternionic coefficients in local neighborhoods and phase regularization. In\naddition a non-marginal aspect of multispectral representation is introduced. Thanks to coherent analysis provided by\nthe quaternionic wavelet transformation, the results obtained indicate the potential of this multispectral representation\nwith magnitude thresholding and phase smoothing in noise reduction and edge preservation compared with classical\nwavelet thresholding methods that do not use phase or multiband information....
This paper analyzes noise reduction using matched filter and wavelet transform in the signals of continuous wave\nradar and pulse radar. The denoising application of wavelets has been used in spectrum cleaning of atmospheric\nradar signals. Matched filter has a strong anti-noise ability; it can also achieve accurate pulse compression in a very\nnoisy environment. This paper analyzes the algorithms of matched filter and wavelets that are used in radar signal\nprocessing to reduce the noise. The simulation results indicate that matched filter has a strong anti-noise ability for\npulse radar and wavelet for continuous wave radar....
This paper presents an algorithm for compositing a high dynamic range (HDR) image from multi-exposure images,\nconsidering inconsistent pixels for the reduction of ghost artifacts. In HDR images, ghost artifacts may appear when\nthere are moving objects while taking multiple images with different exposures. To prevent such artifacts, it is\nimportant to detect inconsistent pixels caused by moving objects in consecutive frames and then to assign zero\nweights to the corresponding pixels in the fusion process. This problem is formulated as a binary labeling problem\nbased on a Markov random field (MRF) framework, the solution of which is a binary map for each exposure image,\nwhich identifies the pixels to be excluded in the fusion process. To obtain the ghost map, the distribution of zero mean\nnormalized cross-correlation (ZNCC) of an image with respect to the reference frame is modeled as a mixture of\nGaussian functions, and the parameters of this function are used to design the energy function. However, this method\ndoes not well detect faint objects that are in low-contrast regions due to over- or under-exposure, because the ZNCC\ndoes not show much difference in such areas. Hence, we obtain an additional ghost map for the low-contrast regions,\nbased on the intensity relationship between the frames. Specifically, the intensity mapping function (IMF) between the\nframes is estimated using pixels from high-contrast regions without inconsistent pixels, and pixels out of the tolerance\nrange of the IMF are considered moving pixels in the low-contrast regions. As a result, inconsistent pixels in both the\nlow- and high-contrast areas are well found, and thus, HDR images without noticeable ghosts can be obtained....
Segmentation of vasculature in retinal fundus image by level set methods employing classical edge detection\nmethodologies is a tedious task. In this study, a revised level set-based retinal vasculature segmentation approach is\nproposed. During preprocessing, intensity inhomogeneity on the green channel of input image is corrected by\nutilizing all image channels, generating more efficient results compared to methods utilizing only one (green)\nchannel. A structure-based level set method employing a modified phase map is introduced to obtain accurate\nskeletonization and segmentation of the retinal vasculature. The seed points around vessels are selected and the\nlevel sets are initialized automatically. Furthermore, the proposed method introduces an improved zero-level\ncontour regularization term which is more appropriate than the ones introduced by other methods for vasculature\nstructures. We conducted the experiments on our own data set, as well as two publicly available data sets. The results\nshow that the proposed method segments retinal vessels accurately and its performance is comparable to state-of-the-art\nsupervised/unsupervised segmentation techniques....
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