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Defining a no-reference image quality assessment by means of the self-affine analysis

MULTIMEDIA TOOLS AND applications 2021年第9期80卷 14305-14320页

作者： Escobar, Jesus Jaime Moreno Matamoros, Oswaldo Morales Reyes, Ixchel Lina Padilla, Ricardo Tejeida Hernandez, Liliana Chanona Inst Politecn Nacl Escuela Super Ingn Mecan & Elect Unidad Zacatenco Mexico City DF Mexico Inst Politecn Nacl Escuela Super Turismo Mexico City DF Mexico

In this paper we propose a novel Blind image Quality Assessment via Self-Affine Analysis (BIQSAA) method by considering the wavelet transform as a linear operation that decomposes a complex signal into elementary blocks at different scales or resolutions. BIQSAA decomposes a distorted image into a set of wavelet planes omega(lambda, phi) of different spatial frequencies lambda and spatial orientations phi, and it transforms these wavelet planes into one-dimension vector omega using a Hilbert scanning. From the vector omega there were obtained their wavelet coefficient fluctuations estimated by the inverse of the Hurst exponent in decibels, whose scaling-law or fractal behavior was obtained by applying Fractal Geometry or Self-Affine Analysis. The scaling exponents calculated for the coefficient fluctuation behavior of image Lena at 24bpp, at 1.375bpp, and at 0.50bpp were H-24bpp = 0.0395, H-1.375bpp = 0.0551, and H-0.50bpp = 0.0612, respectively. Our experiments show that BIQSAA algorithm improves in 14.36% the Human Visual System correlation, respect to the four state-of-the-art No-Reference image Quality Assessments.

关键词： image quality assessment Self-affine analysis signal processing wavelet transform

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Identity Recognition Using wavelet-Based Neural Networks: Review and Comparative Study

Identity Recognition Using Wavelet-Based Neural Networks: Re...

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Decision Aid Sciences and Application (DASA), International Conference on

作者： Eliganti Ramalakshmi Dama Pranati Sreya Tanguturi Poojitha Department of Information Technology Chaitanya Bharathi Institute of Technology Hyderabad India

ISBN: (数字)9798350369106

ISBN: (纸本)9798350369113

wavelet neural networks are a unique combination of wavelet analysis and neural network architectures, thus providing the benefits of advanced signal processing. Unlike traditional methods, WNNs offer a multi-resolution approach that outshines for nonstationary signals; therefore, these are very useful in applications related to areas such as biomedical signal analysis, image processing, and time-series forecasting. Architecture of WNNs enables the replacement of activation functions with wavelet functions for the extraction of localized features, making both processing and interpretation of data much more efficient. Some of the recent improvements in designing WNNs improve its computational efficiency to suit the requirements of real-time applications and also to incorporate it into deep learning frameworks, but still, one of the main challenges faced in WNNs such as computational complexity and scalability need to be addressed. As the historical development, core contributions, and evolving architecture of WNNs is done, this paper represents those along with filling up certain existing research gaps. This research paper addresses future directions regarding hybrid models that combine synergies of CNNs with RNNs as well as WNNs and therefore posits opening its way for enhanced performance across various domains. Researching such an area ensures and guarantees further expansion of applications in WNNs besides strengthening their importance in signal processing in the modern arena.

关键词： wavelet transforms Neural networks Computer architecture Thermal sensors wavelet analysis Feature extraction Real-time systems Computational efficiency Internet of Things Noise measurement

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An Improved Digital signal processing for signal Enhancement and Interference Mitigation

An Improved Digital Signal Processing for Signal Enhancement...

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Automation and Computation (AUTOCOM), International Conference on

作者： Intekhab Alam Paramjit Baxi Barnali Sahoo Mohan Garg Roopashree R Thaj Mary Delsy T Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Centre of Research Impact and Outcome Chitkara University Rajpura Punjab India Department of Computer Science and Engineering Siksha ‘O’ Anusandhan (Deemed to be University) Bhubaneswar Odisha India Chitkara Centre for Research and Development Chitkara University Baddi Himachal Pradesh India School of Sciences Chemistry and Biochemistry JAIN (Deemed to be University) Bangalore Karnataka India Department of Electronics and Communication Engineering Sathyabama Institute of Science and Technology Chennai Tamil Nadu India

ISBN: (数字)9798331542375

ISBN: (纸本)9798331542382

Digital signal processing has become a fundamental tool in signal processing applications, such as speech and image processing, due to its ability to manipulate and examine signals in a digital form skillfully. A primary challenge in signal processing involves the presence of unwanted noise and interference, which can negatively impact the precision and quality of the processed signal. The proposed advanced DSP approach aims to improve signal enhancement and interference mitigation techniques using sophisticated algorithms and methods. The proposed state-of-the-art DSP approach includes noise reduction algorithms, such as adaptive filtering and spectral subtraction, to eliminate unwanted noise from the signal. Additionally, cutting-edge signal processing techniques, such as wavelet transforms and time-frequency analysis, are used for signal enhancement and demising. The proposed approach employs adaptive equalization techniques to mitigate interference from other signals and substantially improve the overall signal quality. The enhanced DSP approach is evaluated through simulation and real-world experiments, demonstrating its effectiveness in improving signal quality and reducing interference considerably. The proposed approach has the potential to significantly improve the accuracy and reliability of various signal processing applications considerably, making it a valuable tool in fields such as telecommunications, audio and video processing, and medical signal analysis.

关键词： Wireless communication wavelet transforms Time-frequency analysis Accuracy Prevention and mitigation signal processing algorithms Interference Digital signal processing wavelet analysis Telecommunications

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Feature Level Medical image Fusion with Deep Learning

Feature Level Medical Image Fusion with Deep Learning

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2023 International Conference on Evolutionary Algorithms and Soft Computing Techniques, EASCT 2023

作者： Prathipa, R. Ramadevi, R. Sathyabama Institute of Science and Technology Chennai India Saveetha School of Engineering Department of Biomedical Engineering Chennai India

ISBN: (纸本)9798350313413

In recent years, medical image fusion has emerged as a comprehensive analytical method that frequently employs various modification techniques. The objective of multi-modal medical image fusion is to enhance image quality while preserving specific characteristics by combining images from different imag- ing modalities. This field encompasses cutting-edge domains such as image processing, computer vision, pattern recognition, ma- chine learning, and artificial intelligence. Furthermore, medical image fusion has found extensive clinical applications, enabling healthcare professionals to gain a deeper understanding of lesions by integrating medical images from multiple modalities. In this study, we propose the utilization of a Grey Wolf algorithm, which comprises various learning models, to minimize squared error and optimize the process through weighted entropy during image fusion. The successful results are attributed to a hybridization of the Discrete wavelet Transform (DWT) and the Discrete Curvelet Transform (DCT). By employing Principal Component Analysis, Grey Wolf Optimization, and a Recurrent Neural Network, we have developed a novel fusion model for unsupervised categorization known as GWO-RNN. This model is designed to work with multi-modality images that incorporate structural and sparse constraints. The flexibility and precision of image fusion are further enhanced through correlation and covariance analysis using eigenvalues and eigenvectors. Matlab simulations of the proposed workbench are conducted, comparing it against state-of-the-art methods to assess performance metrics, including peak signal-to-noise ratio, mean square error, correlation, and accuracy. The proposed workbench not only meets but surpasses all performance benchmarks while also reducing computational overhead. © 2023 IEEE.

关键词： discrete curvelet transform Discrete wavelet transform Grey Wolf Optimization using a Recurrent Neural Network (GWORNN) Principal Component Analysis

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wavelet-Based Data Compression for Remote Sensing and image processing

Wavelet-Based Data Compression for Remote Sensing and Image ...

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Optimization Computing and Wireless Communication (ICOCWC), International Conference on

作者： Dhyan Chandra Yadav Atul Dadhich Ananya Saha Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Department of Electrical Engineering Vivekananda Global University Jaipur India Department of Computer Science and Information Technology Jain (Deemed to be University) Bangalore India

wavelet-based totally records compression is a form of information compression used to method far flung sensing and picture processing. This technique makes use of wavelets, that are mathematical features that divide a signal into separate frequency components, and gift the signal as a sum of the components. wavelet-primarily based compression permits for compression of statistics into a smaller, extra efficient form without sizeable lack of best. furthermore, it could additionally produce precise reconstructions of the original sign or image, making it a feasible tool for remote sensing and image processing. This approach is utilized in a selection of applications including medical imaging, television transmission, satellite tv for pc imagery and far flung sensing. via utilising wavelets, more efficient and special records may be received. moreover, due to its low time complexity, wavelet-based compression is ideal for processing big quantities of data speedy and efficaciously.

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wavelet Based Random Noise Removal from Color images Using Python

Wavelet Based Random Noise Removal from Color Images Using P...

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Advances in Electrical, Computing, Communication and Sustainable Technologies (ICAECT), International Conference on

作者： Devanand Bhonsle K K Saxena Ruhi Uzma Sheikh Anil Kumar Sahu Pushpendra Singh Tanu Rizvi Department of Electrical Engg. Shri Shankaracharya Technical Campus Bhilai India Department of Electrical Engg. Govt. Engineering College Bilaspur India Department of Electrical Engineering Anjuman College of Engg. & Technology Nagpur India Department of Electronics and Communication Engg. Siddhartha Institute of Technology & Sciences Narapally India Department of Electronics and Telecommunication Engineering Bhilai Institute of Technology Durg India

image de-noising is an essential field in image processing, encompassing a wide range of applications. This is pre-processing task in which unwanted noise signals are removed using different techniques. Noise are unwanted signals which deteriorate the useful information from the image. These information may be edges, ridges, contours are other fine structures. For different applications these details are important. Noise signals may contaminate the image partially or completely. It depends upon the type of noise and its level. Noise may be categories according to its characteristics. The most frequent types of noise signals encountered in image processing include Additive White Gaussian Noise, Speckle noise, salt and pepper noise, Rician noise, random noise, and more. Noise signals introduced in the images during data acquisition, transmission or due to faulty location. Additive white Gaussian noise is one of the most common noise signal which affect almost all the images in a certain extent. In this chapter we apply de-noising technique which is based on wavelet thresholding. wavelet transform is widely recognized as one of the most popular transforms in signal and image processing. It is used in various image processing applications. Thresholding is an essential component in wavelet transform, and it is commonly classified into two types: hard thresholding and soft thresholding. In the chapter we apply soft thresholding technique which outperforms hard thresholding technique.

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Feature-Driven Fusion of Thermal and Visible images for Face Analysis

Feature-Driven Fusion of Thermal and Visible Images for Face...

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Artificial Intelligence and signal processing (AISP), INDIA, International Conference on

作者： Devansh Tikariha Palani Thanaraj Krishnan T. Sunil Kumar School of Computer Science & Engineering Vellore Institute of Technology Chennai Campus India Department of Electrical Engineering Mathematics and Science University of Gävle Sweden

ISBN: (数字)9798350350654

ISBN: (纸本)9798350350661

This study presents a feature-driven fusion method for combining thermal and visible images in facial analysis, leveraging morphological and gradient operations to enhance image quality and information content. By integrating diverse features, including structural components and edge details, the proposed fusion technique offers a comprehensive representation of input images, surpassing traditional methods like Discrete wavelet Transform (DWT) based image fusion. Performance evaluation using metrics such as Peak signal-to-Noise Ratio (PSNR) and Spatial Frequency (SF) demonstrates the superior quality and enhanced texture details achieved through the feature fusion approach. This proposed fusion technique not only enhances visual quality but also enriches the fused images with detailed information, highlighting its potential for various applications in image processing and analysis.

关键词： Performance evaluation Visualization PSNR image edge detection Face recognition Transforms signal processing Thermal analysis Discrete wavelet transforms image fusion

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Remote sensing image denoising based on improved semi-soft threshold

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signal image AND VIDEO processing 2021年第1期15卷 73-81页

作者： Lei, Sai Lu, Mingming Lin, Jieqiong Zhou, Xiaoqin Yang, Xuemei Changchun Univ Technol Sch Mechatron Engn Key Lab Micro Nano & Ultraprecis Mfg Jilin Prov Changchun 130012 Peoples R China Jilin Univ Sch Mech & Aerosp Engn Changchun 130025 Peoples R China

Remote sensing image denoising has important applications in aerospace, geophysical exploration and communication engineering. Traditional wavelet transform cannot represent the details and contours of the image texture effectively, because pseudo-Gibbs effect, ringing and blurring of edge details will be produced during de-noising. In this paper, in view of the shortcomings of the existing directional filter design, an eight-direction filter bank that directly decomposes each direction of the image using each directional subband filter is proposed, which is combined with Laplace pyramid transformation to form an optimized contour transformation. A denoising threshold processing algorithm for remote sensing images based on optimized contourlet transformation is proposed. Compared with the traditional denoising method, the improved algorithm has the characteristics of multi-scale and multi-direction, and can better capture the detailed information of the image. The improved semi-soft threshold function is used to process the transformed coefficients, which can better restore the edge information in the image. The results of a series of simulation experiments show that compared with the traditional wavelet threshold function, the contourlet hard threshold function and contourlet soft threshold function, this method achieves better visual effects and higher PSNR (peak signal to noise ratio) while the image is denoised, and the denoising effect is improved by 0.11%. The proposed new threshold function is feasible and can handle the texture information and edge information of remote sensing image well.

关键词： image processing image denoising Contourlet transform Semi-soft threshold Remote sensing image

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image Enhancement Using HSV Color Space, DWT, and BiHE Techniques 6

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

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6th Iraqi International Conference on Engineering Technology and its applications, IICETA 2023

作者： Obaida, Tameem Hameed Kadum, Salman Abd Najjar, Fallah H. Al-Jawahry, Hassan M. Technical Institute of Najaf Al-Furat Al-Awsat Technical University Najaf54001 Iraq School of Computing University Technology of Malaysia Faculty of Engineering Johar Bahru Malaysia The Islamic University Faculty of Engineering Najaf54001 Iraq

ISBN: (纸本)9798350303339

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. © 2023 IEEE.

关键词： image enhancement

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wavelet Analysis of Photoplethysmogram in Zebrafish Larvaе Cardiovascular System Study

Wavelet Analysis of Photoplethysmogram in Zebrafish Larvaе ...

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Digital signal processing and its applications (DSPA), International Conference on

作者： Artem E. Slavin Anastasia V. Guryleva Valeria I. Bukova Leonid A. Shuman Alexander B. Burlakov Faculty of Space Research Lomonosov Moscow State University Moscow Russia Acoustooptical Spectroscopy Lab Scientific and Technological Centre of Unique Instrumentation of the Russian Academy of Sciences Moscow Russia Department of Human and Animal Anatomy and Physiology Tyumen State University Tyumen Russia Faculty of Biology Lomonosov Moscow State University Moscow Russia

The work is aimed on the development of advanced algorithms for analyzing the photoplethysmogram signal, which characterizes cardiac activity. We have designed an algorithm that separates individual pulse waveforms from the photoplethysmogram, even in the presence of artifacts caused by pathological changes in zebrafish cardiac activity, due to cadmium exposure. The proposed algorithm is based on digital image processing and discrete wavelet transform; it enables reliable separation of individual pulse waveforms. The data obtained can then be processed using conventional techniques, providing accurate pulse wave characteristics for various applications, including toxicology studies, drug development and developmental biology research.

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