Vector median-rational hybrid filters for multichannel image processing

A new multichannel filtering approach is introduced and analyzed in this paper. These filters are based on rational functions (RF) using fuzzy transformations of the Euclidean distances among the different vectors to adapt to local data in the image. The output is the result of vector rational operation taking into account three sub-functions, such as two Fuzzy Vector Median (FVM) sub-filters and one Fuzzy Center Weighted Vector Median Filter (FCWVMF). Simulation studies indicate that the filters are computationally attractive and have excellent performance such as edge and details preservation and accurate chromaticity estimation.

Nowadays, a lot of autonomous unmanned underwater vehicles (AUV) are created in the Institute of Marine Technology Problem FEB RAS. Most of all include side-scan sonar (SSS) EdgeTechSonar with 32-bit resolution. Robots solve different problems. There are bottom map construction, monitoring of underwater channels, tubes, mineral exploration. Signal processing requires complex numerical resources. Unfortunately, AUV doesn’t solve these problems on the board independently. Real signal requires additional processing. Hence, sonar data is processed in the laboratory. Currently researchers have large databases of AUV. Data processing in the laboratory is inconvenient. If engineer adds additional processing algorithm he changes all local software. In this paper authors use cloud approach for solving this problem. The data from SSS EdgeTechSonar upload directly to the cloud service. Afterwards, user chooses any filter for sonar image processing. The cloud service includes a database of sonar images with its geolocation. Moreover, user uses different filters for image processing. Filters in cloud service are executed with parallel technology for fastest processing. There are 512 parallel processors with mpi-technology and several GPU-processors. Choice of technology depends on filter properties. Authors research some filters for sonar image processing and design special parallel algorithms for each of them.

Thee Median-Rational Hybrid Filter (MRHF) has been recently introduced as a new class of nonlinear filters and successfully applied to image filtering problems. The main characteristics of the MRHF are the good noise attenuation property in smooth areas and the preservation of edges end details. In fact, in changing areas the noise attenuation is traded for a good response to the change. Moreover, the median filters effectively remove the impulsive noise while the rational filter performs well in relatively high SNR Gaussian contaminated environments. Another relevant characteristic is that MRHF's usually act on small windows and thus require a reduced number of operations, resulting in simple and fast filtering structures. In this paper we present a programmable hardware implementation for the MRHF's. Exploiting the features of the dynamic logic families it is possible to achieve high speed and compactness, while keeping the power dissipation very low.

A new class of filters for multichannel image processing is introduced and analyzed. This class constitutes a generalization of vector directional filters. The proposed filters use fuzzy transformations of the angles among the different vectors to adapt to local data in the image. The principle behind the new filters is explained and comparisons with other popular nonlinear filters are provided. The specific case of color image processing is studied as an important example of multichannel image processing. Simulation results indicate that the new filters offer some flexbility and have excellent performance.

This paper presents a new image fusion technique that employs shearlet-based edge enhancement and adaptive fusion rule to improve the visual quality and information retention of fused images. The new method first applies shearlet-like edge detection filters to the input images with emphasis on finer details such as edges. The enhanced images are then adaptively fused based on local image properties, including texture and edge sharpness, to obtain the fusion weights. The performance of the method is rigorously tested using quality measures such as Mutual Information (MI), Entropy, Peak Signal-to-Noise Ratio (PSNR), and Structural Similarity Index (SSIM). The execution time of each algorithmic process (enhancement, fusion, and computation of metrics) is also recorded, which provides information on its efficiency. The experimental results confirm that the new method achieves high-quality fusion with preservation of edges and finer details and retention of essential information from the original images. The method is promising for applications in medical imaging, remote sensing, and computer vision, where image clarity and preservation of details are significant.

Robust adaptive vector filtering algorithms applicable to color and multichannel image processing are proposed. They are based on the use of Q-parameter that is a vector analog of quasirange. Considered algorithms have a good combination of properties: effective noise reduction, ability to remove spikes, edge and detail preservation, and low computational complexity. Their characteristics are evaluated quantitatively and compared to non-adaptive counterparts. Advantages of proposed algorithms are also demonstrated by simulated image processing results.

Rail track, t urnout and level crossing are critical parts of railway components. So faults of rail surface are directly related to the operation safety. The defects occurring in switches can lead to accidents and the derailment of the train. Therefore, it is necessary to utilize advanced technology to monitor rail damage of the switch and level crossing zones. In this study, the switch and level crossing detection are performed with vision based contactless image processing techniques. Edge detection algorithm, image processing filters, morphological feature extraction, Hough transform and hierarchical SVM classifier are utilized to detect turnout and level crossing regions in the railway images. While switch points are detected, images of the area in front of the train are continuously taken from camera placed on the train. These images contain foreign objects such as buildings, trees, etc. The foreign objects existing in the railway image make it difficult to detect accurately the rail track. The turnout and level crossing detection cannot also be performed using the information of the rail track that cannot be correctly detected. The several processes, such as some image processing filters and morphological feature extraction, have been performed to solve this problem. With destroying the foreign objects from the railway images, both the accuracy rate of the proposed method has been increased and real-time implementation of the method is provided. The Hough transform is utilized to detect rail track, switch and level crossing in the proposed method. The turnout detection is performed by applying SVM classifier to railway image. The proposed method provides real-time rail track, level crossing and turnout detection. Furthermore, this method has gained dynamic feature due to not be utilized any template image for switch detection. When evaluated from these aspects, both the proposed method is superior to other methods in literature and results were obtained in a shorter time thanks to applying ROI Segmentation to the railway image.

Image processing techniques have been used in processing of potential fields data in order to delineate the edges of the anomalous sources. In this research, firstly the 2D Gabor filter was introduced. Then its operation was investigated on the three magnetized prisms which were at the three different depths and orientations. The effects of body wavelength, standard deviation and body orientation in the Gabor filter on the detection of edge bodies were studied. Investigations showed that the Gabor filter is a low pass filter. Afterwards, the Gabor filter was applied on the aeromagnetic data of Khoram Abad region in the southwest and ground magnetic data from Kashmar region in the northeast of Iran. The results show that the trend of geological structures is NW-SE which is in concordance to those obtained from other filters as well as geological map of the region. 1 2D Gabor filter The boundary of the geological structures can be determined by processing of potential field spectrums .Most filters which have been used for edge detection are based on the different degree of derivatives. The derivatives cause noises and signals amplify together which decrease resolution of edges. Therefore, using filters without any derivative terms is a prime objective. The Gabor filter has not any derivative terms in its mathematical structure. Denis Gabor first introduced the filter for image processing. This filter is linear and has been used for analyzing details of an image or its texture. The characteristics of the Gabor filter is to obtain special frequencies in a certain point or a region. Mostly this filter has been used for analyzing human vision system. The Gabor filter has two parts, real and imaginary in exponential forms which are multiplied with a Gaussian function. The real part has been used for data filtering. The Gabor filter response in spatial domain is defined by convolution of data in the Gabor matrix. The Gabor filter is a low pass filter which attenuates noises while it detects the edges of the deep anomalies in a certain direction. The Gabor filter in mathematics is in the group of filter transformation. On this basis, this filter can process images in different directions and frequencies. The Gabor filter has many applications in image processing such as texture analysis, fingerprint detection, edge detection, document processing and so on. The 2D Gabor filter was also used for identification of the edges of the geological structures and faults. For determination of the geological trends and faults, we must apply the Gabor filter in different directions and frequencies and standard deviations. In this article, we first introduce the Gabor filter and its characteristics. Afterwards this filter is applied on the aeromagnetic data from Khoram Abad and ground magnetic from an iron mine at Kashmar regions in the the southwest and northeast of Iran respectively. 2 The real data We prepared the following maps from the real data in Khoram Abad region: the map of the total magnetic intensity, the reduction to the pole of aeromagnetic map and the map resulted from the application of the Gabor filter on the residual aeromagnetic map of the region. From the latter map, the trend and edges of the geological structures were identified. The other trends may be detected by changing the various parameters of the filter specially its direction. The trend of the geological structures which is revealed by the Gabor filter is northwest- southeast. If the Gabor filter is applied with the other edge filters such as derivative of tilt angle, analytic signal filters, we can identify the boundaries better. The Gabor filter on the data from Kashmar region, delineates the boundary of the iron ore body at the studied area.

Through the combination of linear finite impulse response (FIR) filters and weighted order statistic (WOS) filters, the authors introduce a new class of nonlinear filters called FIR-WOS hybrid (FWH) filters. This class of filters, including FIR filters, WOS filters, and various kinds of FIR median hybrid (FMH) filters, consists of a few linear FIR subfilters and a WOS filter operated over the outputs of the subfilters. Motivated by the backpropagation algorithm used in neural networks, an adaptive algorithm is derived in the binary domain for determining optimal FWH filters under the mean absolute error criterion. Simulation results in image processing demonstrate that adaptive FWH filters perform as well as linear filters in Gaussian noise and better than adaptive WOS filters in impulsive noise. >

Shock filter is an important type of the filters in image processing. In recent years, research on shock filters has been an interested topic in signal and image processing. In this paper we propose a method to realize the shock filter by CNN (Cellular Neural Network). The method offers fast processing time in comparison with that on the serial digital computers and can be used in many real-time image processing systems. We focus on development of the shock filter algorithm realizing edge enhancement and de-noising on the Bi-I CNN visual computer. Besides presenting the algorithm, many experiments on Bi-I are given for demonstration.

Optimization is an important subject in solving scientific and engineering problems. One conventional searching technique for finding the global minimum is to use gradient descent, which finds the direction for the next iteration from the gradient of the objective function. For complicated problems, the gradient descent technique often gets stuck at a local minimum where the objective function has surrounding barriers. In addition, the complexity of most combinatorial optimization problems increases dramatically with the problem size and makes it very difficult to obtain the globally optimal solution in a reasonably computational time. Complicated optimization problems could also be solved by using branch-and-bound searching method [1], or by relaxing some constraints and solving the simplified problems [2]. Several attractive methods have been reported to help the solutions of the optimization problems to escape from local minima [3 4, 5, 6, 7, 8, 9, 10]. Simulated annealing is one of the popular approaches which is widely applicable to the combinatorial optimization problems [3, 4]. Simulated annealing is a generalization of iterative improvement methods in that it accepts, with a non-zero but gradually decreasing probability, deterioration in the cost function of an optimization problem. A solution from simulated annealing is close to the global minimum within a polynomial upper bound for the computational time and is independent of the initial condition. Mean field annealing is a deterministic approximation to simulated annealing [7]. It analytically approximates the relevant Boltzmann distribution and can execute at 50 times faster than simulated annealing for some problems. The quality of the solutions produced by mean field annealing is highly dependent on the choice of the initial temperature, the cooling speed and the final temperature. So far, simulated annealing has been successfully reported in layout generation of VLSI circuits [11, 12], test pattern generation [13], integrated-circuit delay reduction [14], image segmentation [15] and noise filtering in image processing [16].

A simplified nonlinear transient analysis method for gas bearings was recently published by the authors (Hassini, M. A., and Arghir, M., 2012, “Simplified Nonlinear Transient Analysis Method for Gas Bearings,” J. Tribol., 134(1), 011704). The method uses the fact that linearized dynamic characteristics of gas bearings, namely the impedances, can be approximated by rational transfer functions. The method gave good results if the rational transfer function approach approximated the linearized dynamic characteristics well. Indeed, each of the four complex impedances Zαβ,α,β={x,y} had one or two poles depending on the order of the rational function that were used. These poles appear as supplementary eigenvalues of the extended matrix of the homogeneous system of first order differential equations describing the model of the rotor. They govern the stability of the dynamic model in the same way as the original eigenvalues do and therefore they impose non-negligible constraints on the rational function approximation of the impedances of gas bearings. The present improvement of the method overrides this problem. The basic idea is to impose the same set of poles for Zxx, Zxy, Zyx, and Zyy. By imposing this constraint, the poles are stable and the introduction of artificial instability or erratic eigenvalues is avoided. Campbell and stability diagrams naturally taking into account the variation of the dynamic coefficients with the excitation frequency can now be easily plotted. For example, the method is used for analyzing the stability of rigid and flexible rotors supported by two identical gas bearings modeled with second order rational transfer functions. The method can be applied to any bearing or seal whose impedance is approximated by rational transfer functions.

A measure of success for any learning algorithm is how useful it is in a variety of learning situations. Those learning algorithms that support universal function approximation can theoretically be applied to a very large and interesting class of learning problems. Many kinds of neural network architectures have already been shown to support universal approximation. In this paper, we will provide a proof to show that Fuzzy ART augmented with a single layer of perceptrons is a universal approximator. Moreover, the Fuzzy ARTMAP neural network architecture, by itself, will be shown to be a universal approximator.

An increasing number of free software tools have been made available for the evaluation of fluorescence cell micrographs. The main users are biologists and related life scientists with no or little knowledge of image processing. In this review, we give an overview of available tools and guidelines about which tools the users should use to segment fluorescence micrographs. We selected 15 free tools and divided them into stand-alone, Matlab-based, ImageJ-based, free demo versions of commercial tools and data sharing tools. The review consists of two parts: First, we developed a criteria catalogue and rated the tools regarding structural requirements, functionality (flexibility, segmentation and image processing filters) and usability (documentation, data management, usability and visualization). Second, we performed an image processing case study with four representative fluorescence micrograph segmentation tasks with figure-ground and cell separation. The tools display a wide range of functionality and usability. In the image processing case study, we were able to perform figure-ground separation in all micrographs using mainly thresholding. Cell separation was not possible with most of the tools, because cell separation methods are provided only by a subset of the tools and are difficult to parametrize and to use. Most important is that the usability matches the functionality of a tool. To be usable, specialized tools with less functionality need to fulfill less usability criteria, whereas multipurpose tools need a well-structured menu and intuitive graphical user interface.

Evaluation of resolution characteristics of digital intraoral radiographic images using a task transfer function.