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Deconvolution has become one of the most used methods for improving spectral resolution, and blind deconvolution as a typical method has been researched widely. However, the predefined point spread function (PSF) used in blind deconvolution method is not known exactly in practice. In general, the PSF is estimated simultaneously from the observed spectrum, but it becomes difficult when the spectroscopic...
The density-based clustering algorithm DBSCAN is a fundamental technique for data clustering with many attractive properties and applications. However, DBSCAN requires specifying all pair wise (dis)similarities among objects that can be non-trivial to obtain in many applications. To tackle this problem, in this paper, we propose a novel active density-based clustering algorithm, named Act-DBSCAN,...
We present a new greedy algorithm for super-resolution. Given the low-frequency part of the spectrum of a sequence of impulses, our objective is to estimate their positions. The backbone of our work is the fundamental work of Slepian et al. involving discrete prolate spheroidal wave functions and their unique properties. By its greedy nature, our work differs from the approach of Candès et al. based...
In this paper we proposed a new framework for obtaining the spongy and cortical bones from the MRI data. The method focuses on the accurate extraction of the edges of the target tissues, which is the main drawback of the previous works. This framework first limits the searching area for the bone voxels from the whole data to a small strip around the edges of the cortical and spongy bones then applies...
We consider a localization problem of multiple sources from range and angle measurements. To exploit the heterogeneity of the information we formulate the problem as an optimization over an edge-kernel matrix, in which angle and distance information are decoupled. The algorithm consists of estimating the Euclidean structure of the network by means of the angle-kernel (Euclidean kernel derived from...
The blurring in image comes either from the acquisition noise, or from image editing operation. The produced adverse noise during acquisition need to be eliminated, and the blurring generated by editing should be known in digital forensics, so the blur kernel recovery is significant in community of image processing and computer graphics. In the log-fourier domain, the images before and after blurring...
Operating system (OS) noise refers to the interference experienced by an application due to activities inside an operating system. OS noise can significantly hurt the performance of applications, especially parallel applications. We implemented a tool, noise test, to study the characteristics of OS noise on the Godson-3A shared-memory multicore platform running Linux. We found that OS noise was balanced...
Wireless location has now gained considerable attention. One of the main problems facing accurate location in wireless communication systems is non-line-of-sight (NLOS) propagation. There are parametric and non-parametric methods to cope with NLOS errors. Compared with the parametric method, the non-parametric method can provide a unified solution with an optimal performance for different channel...
In this paper, a new multi-scale deblurring method is proposed to remove the motion blur. The method estimates the blur kernel by an alternative algorithm at scales from coarse to fine. After the blur kernel is estimated in the finest scale, the blurred image is restored via image deconvolution. To remove the ringing artifacts, we propose a smooth regions constraint. Combining with the noise prior,...
A method is proposed for analysing the geometric and logical structure of pages in a typical single-column book. A Gaussian blur combined with thresholding is used to form connected components which nominally represent words. A bottom-up nearest-neighbour approach is used to find textual lines, and a manually-defined line length parameter is used to remove marginal noise and find the page frame. A...
Magnetic resonance (MR) images can be used to detect lesions in the brains of multiple sclerosis (MS) patients and is essential for diagnosing the disease and monitoring its progression. An automatic method is presented for segmentation of MS lesions in multispectral MR images. Firstly a PD-w image is subtracted from its corresponding T1-w image to get an image in which the cerebral spinal fluid (CSF)...
Spectral unmixing is a useful technique in fluorescence imaging for reducing the effects of background fluorescence (BF), also called autofluorescence (AF), and separating multiple fluorescence probes. But it is complicated by the significant overlap of the fluorophore emission spectra, and the strong BF signal is often highly mixed with all multi-target fluorescences and can have a confusing effect...
The goal of the work was to understand the impact on clinical measures of timing resolution for time-of-flight (TOF) fully-3D iterative reconstruction. We compared TOF impact to that from use of a PET detector response model. Sixteen FDG-avid features in 13 whole-body FDG studies acquired on a TOF-capable PET/CT system were used. A NEMA image quality phantom was also studied for comparison and reconstruction...
This paper shows that the FBP algorithm is able to provide a solution to a weighted least-squares problem. Therefore, the projection noise model can be included in the FBP reconstruction. Bayesian information can also be incorporated in the FBP reconstruction.
Image matching based on contour is an important issue in computer vision, navigation and pattern recognition. The image matching methods like curvature-based methods and corner-based methods have poor robustness to the contour's noise and distortion, and some matching methods are applied only to closed contours. A novel contour representation and matching algorithm, based on local curvature scale,...
To have a unique solution of an ill-posed inverse problem, the usual way is to embed prior information in terms of regularizer or smoothness criterion. In this work, both the inverse mechanism (the relationship of blur and sharp patches) and the smoothness prior are learned simultaneously from the image itself, in multiple scales. We have shown experimentally that the proposed method outperform the...
Existing dual image deblurring methods usually model blurred image pairs being taken from exactly the same viewpoint and restore a single clear image. This imposes a strong assumption that the latent clear images of both images must be completely identical. In contrast to this restricted scenario, we assume that the restored pair are different, but can be approximated by image warping due to small...
In many applications, meaningful image structures tend to be piecewise smooth rather than band-limited. And edge-preserving image filters are always used to extract such structures and reduce noise. In this paper, a new image filtering method is introduced. We use geodesic distance to compute support weight, and a fast approximation named orthogonal geodesic distance weight is proposed. It greatly...
Image denoising methods have been implemented in both spatial and transform domains. Each domain has its advantages and shortcomings, which can be complemented by each other. State-of-the-art methods like block-matching 3D filtering (BM3D) therefore combine both domains. However, implementation of such methods is not trivial. We offer a hybrid method that is surprisingly easy to implement and yet...
In this brief, fractional 90-degree phase-shift filtering technique is investigated. First, thanks to the two commonly used Liouville and Weyl fractional integral definitions, we present Liouville and Weyl fractional differintegrators which are generalized magnitude-and-phase modulations. Then, paralleling a Liouville fractional differintegrator with a Weyl fractional differ-integrator to obtain a...
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