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The design of MAC unit for DWT implementation

The design of MAC unit for DWT implementation

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International conference on Microelectronics, ICM

作者： C. Souani M. Abid K. Torki R. Tourki Electronics & Micro-Electronics Laboratory Faculty of Sciences of Monastir Tunisia TIMA-CMP Laboratory Grenoble France

High performance FIR filters have applications in several video processing and digital communications systems. Audio and telecommunication applications may use filters at sample rates of 10-30 MHz. High-speed digital data communications or high rate video transmission systems may need preferment filters. This paper deals with the VLSI implementation of a recursive filter. The designed device will he used as a sub-element for the design of a DWT circuit. The performance of the DWT circuit depend on the performance of the MAC unit.

关键词： Discrete wavelet transforms Finite impulse response filter Filter bank Digital filters Adaptive filters Circuits Application software image coding signal resolution Low pass filters

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High speed low power DWT structure with log based FPU in FPGAs

High speed low power DWT structure with log based FPU in FPG...

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International conference on Green Computing, Communication and Conservation of Energy (ICGCE)

作者： D. Vaithiyanathan R. Seshasayanan Dept. of Electronics and Communication Engineering Anna University Chennai India

ISBN: (纸本)9781467361248

wavelet transforms have become one of the basic tools of signal and image processing, digital communications and other applications. Biorthogonal wavelet transform attains further more importance as it allows the construction of symmetric wavelet, which provides the linear phase filter. As the lossy biorthogonal 9/7 (B9/7) transform uses floating point lifting coefficients, the computational complexity of the overall structure gets increased as the levels are increased. This leads to unwanted increment in additional latency in the overall circuit and also hardware realization leads to increased area and power consumption. Hence in this paper, we propose an alternate structure for arithmetic computations involved in the B9/7 DWT which utilizes the power saving attribute of Logarithmic Number Systems (LNS). This LNS adaptation in arithmetic unit results in overall accuracy enhancement with reduced area and power consumption. We also explored the constituent latency and found that the main memory access for the input feed is one of the main factors that increase the latency in the circuit. Hence, we attain a feasible solution of segregating the main memory from the structure, and inputs are fed from temporary memory blocks to the structure. Hence, symmetric extension of the image to be transformed is handled in a way that does not require additional computation or clock cycles; it is expected to provide a reduction in hardware complexity and increase in computational speed. We evaluated the performance and correctness of the architecture by implementing in Xilinx Virtex 6 field programmable gate arrays.

关键词： Discrete wavelet transforms Accuracy PSNR Delays Field programmable gate arrays

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Artificial intelligence for pattern recognition in automated surface engineering

Artificial intelligence for pattern recognition in automated...

引用

International conference on Systems and Informatics (ICSAI)

作者： E. Sheybani S. Garcia-Otero F. Adnani G. Javidi DSP Laboratory Virginia State University Petersburg VA USA

Ability to measure the surface quality in real-time has many applications in manufacturing automation and product optimization, especially in processes in which the surface qualities such as roughness, grain size, thickness of coding, impurities size and distribution, hardness, and other mechanical properties are of importance. Surface analysis in manufacturing environments requires specialized filtering techniques. Due to the immense effect of rough environment and corruptive parameters, it is often impossible to evaluate the quality of a surface that has undergone various grades of processing. The algorithm presented here is capable of performing this comparison analytically and quantitatively at a low computational cost (real-time) and high efficiency. The parameters used for comparison are the degree of blurriness and the amount of various types of noise associated with the surface image. Based on a heuristic analysis of these parameters the algorithm assesses the surface image and quantifies the quality of the image by characterizing important aspects of human visual quality. Extensive effort has been set forth to obtain real-world noise and blur conditions so that the various test cases presented here could justify the validity of this approach well. The tests performed on the database of images produced valid results for the proposed algorithm consistently. This research aims at providing image processing tools for comparison and assessment of a surface processed under different grades of a manufacturing process all the way up to optimal processing. This paper presents the description and validation (along with test results) of the proposed algorithm for surface image quality assessment.

关键词： Surface treatment Noise Algorithm design and analysis Filter banks image quality signal processing algorithms wavelet transforms

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Improving Block Matching and 3 Dimensions (BM3D) Filtering for image Noise Removal Using Discrete wavelet Transformation (DWT)

Improving Block Matching and 3 Dimensions (BM3D) Filtering f...

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Sciences and Techniques of Automatic Control and Computer Engineering MI-STA, IEEE International Maghreb Meeting of the conference on

作者： Warda Kshem Asem Khmag Department of Computer Engineering Faculty of Engineering University of Zawia Az zawia Libya

ISBN: (数字)9798350372632

ISBN: (纸本)9798350372649

In the field of image denoising, Block Matching 3 Dimensions technique (BM3D) stated as a powerful technique that works to remove white Gaussian noise (A WGN). Despite BM3D shows its great success in comparison with the best denoising methods, still its visual quality decreases as the noise density escalates in noisy digital images. Therefore, the need to create a powerful image denoising method is necessary to accomplish the processing of image enhancement applications. The proposed method is introduced by. In this study, discrete wavelet transformation is applied on the LL level of the wavelet decomposition due to its ability to keep the smallest change in the original image. Furthermore, the coefficients of LL will be then divided to small blocks that contain contaminated pixels that suffer from additive white Gaussian noise (A WGN). Comparing with conventional block matching 3D (BM3D) that works well only on the similar patches, our proposed method has the ability to work on similar and dissimilar patches in order to find the inter and intra- correlations between dissimilar pixels. The proposed technique is examined on several digital benchmark images with different noise levels in terms of the quantity of the lost in the original and resulted pixels. In this regard, the performance of the tested images is estimated and then compared proposed with several high quality noise removal techniques according to several assessments such as peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), and the figure of merits (FOM). The high amount of these factors reflects the success of the proposed technique. Further, it is interesting to observe the improvement in the running time where it was far less than the investigated noise removal algorithms.

关键词： Visualization Correlation PSNR Three-dimensional displays Noise Estimation Discrete wavelet transforms

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Multiple scaling factors based Semi-Blind watermarking of grayscale images using OS-ELM neural network

Multiple scaling factors based Semi-Blind watermarking of gr...

引用

IEEE International conference on signal processing, Communications and Computing (ICSPCC)

作者： Ankit Rajpal Anurag Mishra Rajni Bala Department of Computer Science University of Delhi New Delhi India

ISBN: (纸本)9781509027095

In this paper, a multiple scaling factor based Semi-Blind watermarking scheme for grayscale image watermarking using Online Sequential Extreme Learning Machine (OS-ELM) is proposed. Four-level DWT is applied on three standard test images of size 512 × 512. LL4 sub-band coefficients are chosen for watermark embedding. OS-ELM is initially tuned with a fixed number of training data used in its initial phase and size of data block learned in each iteration. The training set is developed by combining the quantized and desired LL4 sub-band coefficients. This training set is fed to OS-ELM for training. The output of OS-ELM is a sequence of predicted coefficients which is sorted and divided into three equal parts. Multiple Scaling Factor (MSF) scheme is used for embedding a binary watermark in a Semi-Blind manner. Results show good visual quality of signed images and good similarity between the extracted and original watermarks from signed and attacked images. The PSNR and BER are found to be well optimized both in case of signed and attacked images. The time for embedding and extraction is in milliseconds, which makes the proposed technique effective for developing real time watermarking applications.

关键词： Watermarking Mathematical model Training data Training Gray-scale Visualization Discrete wavelet transforms

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image Enhancement Using HSV Color Space, DWT, and BiHE Techniques

Image Enhancement Using HSV Color Space, DWT, and BiHE Techn...

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International conference on Engineering Technology and their applications (IICETA)

作者： Tameem Hameed Obaida Salman Abd Kadum Fallah H. Najjar Hassan M. Al-Jawahry Technical Institute of Najaf Al-Furat Al-Awsat Technical University Najaf Iraq Faculty of Engineering School of Computing University Technology of Malaysia Johar Bahru Malaysia Faculty of Engineering The Islamic University Najaf Iraq

image enhancement is vital in computer vision and image processing applications. This study introduces an innovative approach combining HSV color space, Discrete wavelet Transforms (DWT), and Bi Histogram Equalization (BiHE) techniques. By converting the image from RGB to HSV, we separate color and intensity information, allowing us to enhance the intensity component for improved visual quality. The DWT is then applied to decompose the image into frequency sub bands, enabling targeted manipulation of specific frequency components to enhance image details and overall appearance. Additionally, the BiHE technique enhances contrast while preserving local image details. The proposed method was evaluated on various images, including tissue, football, Lena, and yellow lily. The results show that the proposed method achieved high Peak signal-to-Noise Ratio (PSNR) values, with tissue achieving 32.1853 dB, football achieving 30.5452 dB, Lena achieving 27.1462 dB, and yellow lily achieving 25.2015 dB. The Mean Squared Error (MSE) values were also low, with tissue having an MSE of 0.0006, football having an MSE of 0.0009, Lena having an MSE of 0.0022, and yellow lily having an MSE of 0.0031. These results demonstrate the effectiveness of the proposed method in enhancing the visual quality, details, and contrast of the images while maintaining naturalness and avoiding artifacts. Experimental results demonstrate that our method outperforms traditional techniques, significantly enhancing visual quality, details, and contrast while maintaining naturalness and avoiding artifacts.

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Matrix Quadrature Filterbanks for image processing

Matrix Quadrature Filterbanks for Image Processing

引用

IEEE Pulsed Power conference

作者： Jian-ao Lian Yonghui Wang Department of Mathematics Prairie View A&M University Department of Engineering Technology Prairie View A&M University

ISBN: (纸本)9781509063901

An innovative approach is presented to establish bidimensional (2D) linear-phase matrix quadrature filterbanks (MQF) directly from their matrix identities. It is well-known that two-channel quadrature filterbanks (QMF) have been successfully applied to signal and image processing. Bidimensional QMF filterbanks directly deduced from tensor-products of 1D QMF filterbanks is easy, straightforward, and convenient. They possess both the finite impulse response (FIR) and the perfect reconstruction (PR) properties, which are necessary for filter-banks' alias-free as well as magnitude and phase distortion-free characteristics. However, some additional desirable features are missing. These features include directional features, magnitude preservation, energy preservation, and energy compaction. Direct consideration from matrices yields more freedoms for the design of the new ingenuous MQF. A new MQF is designed and is applied to image compression. The rate-distortion performance shows that the proposed matrix wavelet has promising potential applications in image processing.

关键词： image processing Quadrature mirror filters matrices energy compaction QUADRATURE IMPULSE RESPONSE image compression Data compression Tensor products Phase distortion EARTHQUAKE MAGNITUDE WELLS

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High dimensional Bio medical images denoising using wavelet transform and modified bilateral filter

High dimensional Bio medical images denoising using wavelet ...

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International conference on Mathematics, Actuarial Science, Computer Science and Statistics (MACS)

作者： Muhammad Usama Jabbar Ali Waqar Muhammad Awais Zohaib Mushtaq Muhammad Jamshed Abbass Muhammad Abdul Rehman Faisal Saleem Shehzad Ahmed Ahmed Zubair Jan Department of Electrical engineering National University of Sciences and Technology Islamabad Pakistan University degli Studi di Napoli Parthenope Italy Faculty of Engineering and Applied Science Riphah International University Faculty of Mechanical Engineering Wrocław University of Science and Technology

ISBN: (纸本)9781665460729

Biomedical images consist of salient information that helps in diagnosis of diseases. Significant amount of information is lost during process of transmission due to addition of noisy components especially for Computed Tomography (CT) scan, Ultrasound imaging and Magnetic Resonance Imaging (MRI). image denoising is an important and necessary pre-processing phase in image applications. Numerous techniques have been developed for denoising process over decades. The purpose is to remove noise from images. wavelet transform is current state of the art addition to analyze image denoising; able to achieve improved experimental results than from existing techniques. Generalized Gaussian distribution is considered to model noise. In this article, we introduce mix adaptive thresholding with modified bilateral filter. Simulation results demonstrate that attributes of images are improved. The Qualitative and quantitative assessment proves better denoised results for proposed method using parameters like Peak signal to Noise Ratio (PSNR), Mean Opinion Score (MOS) and Structural Similarity Index (SSIM) between the true and estimated biomedical images.

关键词： wavelet transforms Thresholding (Imaging) PSNR Ultrasonic imaging Computed tomography Magnetic resonance imaging Simulation

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Experimental Investigation for Practical Sparsity Number for image Reconstruction Based on SL0 Algorithm in Discrete Frequency Domain

Experimental Investigation for Practical Sparsity Number for...

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International conference on Advanced Information Networking and applications Workshops (AINAW)

作者： Datchakorn Tancharoen Pham Hong Ha Kornkamol Thakulsukanant Vorapoj Patanavijit Faculty of Engineering and Technology Panyapiwat Institute of Management Bangkok Thailand Department of Computer and Network Engineering Faculty of Engineering Assumption University Bangkok Thailand Department of Telecommunications Science Faculty of Science and Technology Assumption University Bangkok Thailand

Compressive Sensing (CS) is known as a new sampling theory that use small number of basis elements for constructing of signals (or images) and these basis elements (so called sparsity number) are very important parameters that approximate how sparsify the image is. Due to several characteristics of each image groups, the sparsity number could be varied and there is unfortunately very little research for this issue. This paper presents two main contributions: First, this paper proposes a practical sparsity number estimation technique using for an image reconstruction for SL0 algorithm based on Discrete Cosine Transform domain (DCT). Second the practical sparsity number of difference image groups is the experiment based on over 2000 images. The DCT is exclusively applied for a sparse representation of images because it is proven as a useful instrument for image analysis and processing. In general, images can be represented by a linear superposition of small number of wavelet elements selected from a suitable filter. The proposed models process the image with Smoothed norm algorithm. This algorithm stated that if signal or image is sufficiently sparse, we can reconstruct it from small amount of none zero basis components. The experiment is comprehensively tested under 2000 sampling images that are categorized in 18 groups by their characteristics. Moreover this sparsity number is practically used for any CS algorithm based on DCT domain.

关键词： PSNR image reconstruction Vectors Discrete cosine transforms Sparse matrices Algorithm design and analysis

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Hyper Spectral image compression using Higher Order Orthogonal Iteration Tucker decomposition

Hyper Spectral Image compression using Higher Order Orthogon...

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IEEE India conference (INDICON)

作者： B. Sucharitha K. Anitha Sheela Electronics and Communication Engineering Jawaharlal Nehru Technological University Hyderabad India

ISBN: (纸本)9781665452724

Hyper spectral images need more memory to store, process, and transmit due to the large amount of information they carry. One of the most effective way to compress hyper spectral images is to represent them as a three-dimensional tensor. Tensors are multi-dimensional Structures. Tensors have a wide range of applications in numerical linear algebra, chemometrics, data mining, signal processing, statics, data mining, and machine learning. For dimensional reduction of tensors, many tensor decomposition methods have been developed, which we can adapt to Hyper spectral image compression. The proposed method uses Discrete wavelet Transform and Higher-Order Orthogonal Iteration Tucker Decomposition method to compress the Hyper spectral image. The simulation results were compared with 3 more tensor decomposition techniques. Four real hyper spectral images were used in the experimentation: Pavia University (610 X 340 X 103), Indian Pines adjusted (145 x 145 x 200), Salinas image (512 x 217 x 224), and Abu-beach (150 X 150 X 102). After processing, the perceptional quality of the Hyper spectral images is evaluated using PSNR and SSIM. When compared to the other three methods, the proposed method offers good PSNR and SSIM with High Compression.

关键词： Tensors image coding Three-dimensional displays Simulation Memory management Termination of employment Transforms

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