The Shiftable Complex Directional Pyramid—Part II: Implementation and Applications

In order to enhance the accuracy of Chinese off-line handwriting recognition, a new method based on the pyramidal dual-tree directional filter bank (PDTDFB) was presented. According to multi-resolution, arbitrarily high direction resolution, low redundant ratio and efficient implementation properties, the PDTDFB transform can effectively capture more edges and contours in image. Using the extracting features with GDD model to measure the KL distance, we get the image retrieval precision rate. In comparison to the scalar wavelet transform, the complex wavelet transform (CWT) and Contourlet transform, the method increases the accuracy about 22.3%, 7.5%, 2.3%, separately.

In this paper a novel method for generating HR (High Resolution) image using PDTDFB (Pyramidal Dual-Tree Directional Filter Bank) and Interpolation has been adopted. Generally, in digital images since the different kinds of noise highly affects various image processing techniques it is always better to perform denoising first. Here, first of all the image is decomposed into two different layers namely low pass sub band and high pass sub band after which denoising is being performed on both the layers so as to smoothen the image. The smoothened image is then interpolated using edge-preserving interpolation and then amplified. Finally, the HR image is being obtained by performing image composition.

. The development of an Image Processing (IP) application is a complex activity, which can be greatly alleviated by user-friendly graphical programming environments. Our major objective is to help IP experts reuse parts of their applications. A first work towards knowledge reuse has been to propose a suitable representation of the strategies of IP experts by means of IP plans (trees of tasks, methods and tools). This paper describes the CBR module of our interactive system for the development of IP plans. After a brief presentation of the overall architecture of the system and its other modules, we explain the distinction between an IP case and an IP plan, and give the selection criteria and functions that are used for similarity calculation. The core of the CBR module is a search/adaptation algorithm, whose main steps are detailed: retrieval of suitable cases, recursive adaptation of the selected one and memorization of new cases. The system’s implementation is presently completed; its functioning is described in a session showing the kind of assistance provided by the CBR module during the development of a new IP application.

A Wavelet Transform (WT) is a mathematical technique that transforms a signal into different frequency components, each analyzed with a resolution that matches its scale. Wavelets are small waves with limited duration and they possess both time and frequency localization, which means they can capture both high-frequency and low-frequency information simultaneously. There are several types of wavelet transforms, and, depending on the application, one may be preferred to the others. For a continuous input signal, the time and scale parameters can be continuous leading to the Continuous Wavelet Transform (CWT) and Discrete wavelets. Image processing is a critical area in modern computing with applications ranging from medical imaging to remote sensing and computer vision. Traditional Fourier analysis has limitations in capturing localized features in images due to its global nature. Wavelet Transform (WT), with its multi-resolution and localized time-frequency representation, provides a powerful alternative for analysing and processing visual information. This paper presents a comprehensive overview of the theory of wavelet transforms, their mathematical foundations, and applications in image processing and signal processing, including compression of images, denoising and feature extraction from images.

This textbook collects a series of research papers in the area of Image Processing and Communications which not only introduce a summary of current technology but also give an outlook of potential feature problems in this area.The key objective of the book is to provide a collection of comprehensive references on some recent theoretical development as well as novel applications in image processing and communications.The book is divided into two parts. Part I deals with image processing. A comprehensive survey of different methodsof image processing, computer visionis also presented. Part II deals with the telecommunications networks and computer networks. Applications in these areas are considered.In conclusion, the edited book comprises papers on diverse aspects of image processingand communications systems. There are theoretical aspects as well as application papers.

The contourlet transform is a new extension to the wavelet transform in two dimensions using non-separable and directional filter banks. The contourlet expansion is composed of basis images oriented at varying directions in multiple scales, with flexible aspect ratios. With this rich set of basis images, the contourlet transform can effectively capture the smooth contours that are the dominant features in natural images with only a small number of coefficients. We begin with a detail study of the statistics of the contourlet coefficients of natural images, using histogram estimates of the marginal and joint distributions, and mutual information measurements to characterize the dependencies between coefficients. The study reveals the non-Gaussian marginal statistics and strong intra-subband, cross-scale, and cross-orientation dependencies of contourlet coefficients. It is also found that conditioned on the magnitudes of their generalized neighborhood coefficients, contourlet coefficients can approximately be modeled as Gaussian variables with variances directly related to the generalized neighborhood magnitudes. Based on these statistics, we model contourlet coefficients using a hidden Markov tree (HMT) model that can capture all of their inter-scale, inter-orientation, and intra-subband dependencies. We experiment this model in the image denoising and texture retrieval applications where the results are very promising. In denoising, contourlet HMT outperforms wavelet HMT and other classical methods in terms of both peak signal-to-noise ratio (PSNR) and visual quality. In particular, it preserves edges and oriented features better than other existing methods. In texture retrieval, it shows improvements in performance over wavelet methods for various oriented textures.

There are many cases where image processing is performed using the neural network. The content of this Chapter is implementation of CNN (Cellular Neural Network) on a chip in a new original digital approach. First, testing of some features of the proposed cell of the neural network is evaluated. Then the design of the basic circuit containing the cell for the CNN will be introduced. The idea is to use it for a more complex chip with image processing application. CNN have been extensively used in various image processing applications. One of the most important problems in signal processing is noise removal. The input signals mostly arrive from the real world and therefore they contain relatively high amounts of noise. The CNN circuits are capable of removing this noise partly. The possibility of noise removing depends on the template (weight) coefficients between the cells. CNN networks are based on relatively simple principles, very similar to biological neurons. The input signals are multiplied by appropriate weights, the weight matrix being given intuitively, as it is typical for CNN networks. Then they are added and multiplied by the transfer function. We describe also the settings of weights coefficients. CNN have been extensively used in various image processing applications ( Matsumoto & Yokohama, 1990 ) or ( Sziranyi & Csicsvari, 1993 ). The main problem of CNN implementations on a chip is the chip area consumption. The most area is reserved for the multiplexer, so we looked for alternative multiplications. We describe the achieved results with the designed chip. During the creation of the chip architecture we proposed and introduced special original coding for the weight coefficients. After simulations we recognized that the results were better with rounding than without it, but we found that the rounding during multiplication was not as important as we previously expected. Therefore we decided – instead of a hardware multiplexer – to use multiplication realized by simple gate timing of the special signal. The circuit was designed as a digital synchronized circuit. For the design and simulation we used the Xilinx tool. The cell is based on sub-circuits as it will be shown in detail. The Chapter describes the designed circuit. We introduce also our novel simulator for the CNN using the program tool Visual Basic for Application, and its algorithm which was based on the same principle as the planned designed circuit. The network can process the patterns with 400 points of recognition. O pe n A cc es s D at ab as e w w w .ite ch on lin e. co m

In this paper, we address the problem of classifying binary objects using a cascade of a binary directional filter bank (DFB) and a higher-order neural network (HONN). The binary DFB receives as input a binary image and returns as output a binary subband representation. Because processing is performed on a finite field, the DFB is able to operate efficiently. Furthermore, the DFB provides a representation that delineates the directional components in the image, which enables the HONN to exploit the underlying shape of the object effectively. The paper provides a description of the new binary DFB and its use with the HONN, all in the context of object classification.

PrefaceThis issue of Proceedings gathers the papers presented at 2019 4th International Conference on Intelligent Computing and Signal Processing (ICSP 2019) held in Xi’an, China during March 29-31, 2019. ICSP 2019 is an international conference covering research and development in the field of intelligent computing and signal processing and participation from all over the world.More than 400 papers were finally accepted after a double blinded peer review process by international reviewers and academic committee members. Divided into 4 chapters, the papers provide a wide spectrum of researches on wide range of intelligent computing and signal processing. The chapters are devoted to Algorithm and Data Mining, Signal and Image Processing, Automation Engineering and Intelligent Application, Computer Modeling and Performance Structure. Specific research results by conference participants were presented and examined in the light of the frameworks outlined above, which is of interest to academics, researchers and professionals in this field.Two keynote speeches were presented from Prof. Weihua Zhuang, University of Waterloo, Canada, whose topic was about Service Provisioning in 5G Communication Networks; Prof. Nagula Sangary, University of Waterloo & Prudential Technology Ltd., whose topic was about Trends and Challenges in Terrestrial Satellite Wireless Communication Systems in mm-Wave range. All the talks were very impressive for the high level of professionalism, and in many cases original ideas and activities have been accomplished or proposed.List of Committees are availble in this PDF.

This article reviews the various image processing techniques in MATLAB and also hardware implementation in FPGA using Xilinx system generator. Image processing can be termed as processing of images using mathematical operations by using various forms of signal processing techniques. The main aim of image processing is to extract important features from an image data and process it in a desired manner and to visually enhance or to statistically evaluate the desired aspect of the image. This article provides an insight into the various approaches of Digital Image processing techniques in Matlab. This article also provides an introduction to FPGA and also a step by step tutorial in handling Xilinx System Generator. The Xilinx System Generator tool is a new application in image processing and offers a friendly environment design for the processing. This tool support software simulation, but the most important is that can synthesize in FPGAs hardware, with the parallelism, robust and speed, this features are essentials in image processing. Implementation of these algorithms on a FPGA is having advantage of using large memory and embedded multipliers. Advances in FPGA technology with the development of sophisticated and efficient tools for modelling, simulation and synthesis have made FPGA a highly useful platform.

Filter Banks traditionally designed by the factorization method which is complex and time consuming. Singular valued decomposition technique is introduced to design Linear Phase Orthogonal filter bank which yields efficient and fast results in time critical applications. Signal splitting and efficient reconstruction of the original signal is studied achieving perfect reconstruction condition due to the orthogonal nature of designed filter bank. Ultimate aim of proposed methodology is to design two approximation scenarios in terms of square matrices which apparently maps into the filter bank matrix. Singular valued decomposition is iteratively employed to compute both of these square matrices until achievable convergence is achieved between to alternate iterations. Proposed algorithm starts with the design of least square filter bank and ends with the filter bank which ensures orthogonality conditions as well as all filters in the filter bank satisfying linear phase condition. Thus, faster design of the filter bank along with linear phase orthogonal condition yields multiple applications in signal processing, image processing, communication etc.

Massively deployed inside Sony PS3 platforms, the STI Cell Broadband Engine is a multi-core processor with a PowerPC host processor (PPE) and 8 synergic processor engines (SPEs). In this paper, we describe three image processing applications which we implemented on the Cell BE. We report the performance measured on one Cell blade with varying numbers of synergic processor engines enabled, and with varying application parameters. These results were compared to the results obtained on the Cell BE running a single PPE and with all 8 SPEs disabled. The results reveal the superior performance of image processing and SIMD-prone applications on the 9-core Cell BE processor over conventional RISC processors.

Fractional calculus, particularly fractional integral operators, has gained significant attention in recent years due to its wide-ranging applications in various scientific disciplines.This paper provides a comprehensive review of the latest advancements in fractional integral operators, covering theoretical developments, practical applications, and computational methods.We explore the mathematical foundations of fractional integrals, discuss their applications in diverse fields such as signal processing, mathematical physics, and image processing, and examine state-of-the-art computational techniques for their efficient computation.Additionally, we highlight current research trends and future directions in the field of fractional integral operators. I. INTRODUCTIONFractional calculus, a branch of mathematics that generalizes the concepts of differentiation and integration to non-integer orders, has garnered increasing attention in recent years due to its versatile applications across diverse scientific disciplines.Central to the theory of fractional calculus are fractional integral operators, which play a pivotal role in describing non-local and memory-dependent phenomena in complex systems.In this paper, we present a comprehensive review of the latest advancements in fractional integral operators, encompassing theoretical developments, practical applications, and computational methods.Theoretical Foundations of Fractional Integrals, Fractional integral operators, such as the Riemann-Liouville and Caputo integrals, are fundamental tools in fractional calculus.We begin by examining the mathematical foundations of fractional integrals, including their definitions, properties, and relationships with fractional derivatives [1,2].Additionally, we explore important theoretical results related to fractional integrals, such as their behaviour in function spaces and their connection to fractional differential equations [3,4].Applications of Fractional Integrals, Fractional integral operators find applications across a wide range of scientific disciplines, including signal processing, mathematical physics, and image processing.Recent research has demonstrated their utility in modelling anomalous diffusion phenomena [5], describing fractal behaviour in complex systems [6], and analysing non-local effects in viscoelastic materials [7].Moreover, fractional integrals have been applied to noise reduction, feature extraction, and data compression in signal and image processing applications [8,9].Computational Methods for Fractional Integrals, Efficient computation of fractional integrals is essential for their practical use in scientific research and engineering applications.We survey state-of-the-art computational methods for approximating fractional integrals, including numerical integration techniques, spectral methods, and fractional calculus software packages [10,11].Additionally, we discuss challenges and considerations in implementing these methods and provide insights into their computational complexity and accuracy.Current Research Trends and Future Directions, despite significant advancements in the theory and applications of fractional integral operators, several research avenues remain unexplored.Current research trends in the field include the application of fractional calculus to machine learning and data science [12], as well as the development of novel computational methods for solving fractional differential equations [13].Chinta Mani Tiwari (2006).Given A note on Dirac delta function [14], again Chinta Mani Tiwari.(2007).Worked on the Neutrix product of three distributions [15], recently Chinta Mani Tiwari (2023).explained Generalized function and distribution [19].Future research directions may focus on exploring emerging applications and addressing theoretical challenges in the analysis of complex systems using fractional integral operators.In summary, fractional integral operators represent a powerful mathematical tool with widespread applications in science and engineering.By providing a comprehensive overview of theoretical foundations,

Images captured during sand-dust weather conditions suffer from severe color distortions, loss of details, and noise, leading to significantly reduced visibility. These degradations disrupt key vision tasks, such as scene surveillance and intelligent transport systems. Existing restoration methods struggle to handle color distortion, recover fine details, limiting their applicability in real-world scenarios. In this paper, we propose a novel encoder-decoder framework that integrates spatial and frequency-based processing to address these challenges. At its core, our Frequency Selection and Weighting (FSW) module adaptively enhances informative frequency components while sup-pressing degradation-induced noise. We introduce a Multi-Scale and Directional Filter Bank (MSDFB) to distinguish local fine details from global structural patterns, enabling the retrieval of object shapes and textures. Additionally, we incorporate a Multi-Head Self-Attention (MHSA) mech-anism on the extracted feature map from the FSW module to focus on the most salient features. Extensive experiments demonstrate that our approach outperforms state-of-the-art methods across both synthetic and real-world datasets, including cross-dataset validation, achieving superior perceptual quality and significantly enhancing object detection performance in the restored images with lesser parameters.

Target Recognition Based on Directional Filter Banks and Higher-Order Neural Networks