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Clustering-Based 3-D-MAP Despeckling of SAR images Using Sparse wavelet Representation

IEEE GEOSCIENCE AND REMOTE SENSING LETTERS 2022年 19卷 1页

作者： Aranda-Bojorges, Gibran Ponomaryov, Volodymyr Reyes-Reyes, Rogelio Sadovnychiy, Sergiy Cruz-Ramos, Clara Inst Politecn Nacl ESIME Mexico City 04260 DF Mexico Inst Mexicano Petr Mexico City 07730 DF Mexico

image denoising is considered an effective initial processing step in different imaging applications. Over the years, numerous studies have been performed in filtering for different kinds of noises. The block matching with 3-D group filtering has added a new dimension and better results for denoising techniques. This work aims to establish a novel denoising method for multiplicative (speckle) noise employing 3-D arrays resulted from gathering similar patches in clustered areas of an image through the sparse representation based on discrete wavelet transform (DWT) and maximum a posteriori (MAP) estimator technique. Experimental results justified a good quality of the filtered image by the novel framework, which appears to demonstrate better denoising performance against state-of-the-art algorithms according to the objective criteria [peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), and edge preservation index (EPI)] values and subjective visual perception.

关键词： Three-dimensional displays Radar polarimetry Speckle image edge detection Discrete wavelet transforms Noise reduction Indexes Clustering methods filtering image processing maximum a posteriori (MAP) estimator speckle synthetic aperture radar (SAR) wavelet transforms

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Development of a speech dataset for emotion recognition and analysis

Development of a speech dataset for emotion recognition and ...

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Smart World Congress (SWC), IEEE

作者： Yi Wu Haoming Liu School of International Communication and Arts Hainan University Haikou China Department of Systems Engineering and Engineering Management The Chinese University of Hong Kong Hong Kong China

ISBN: (数字)9798331520861

ISBN: (纸本)9798331520878

In recent years, with the development of technologies such as signal processing, machine learning, and deep learning, speech emotion recognition has found wide applications in mental health, educational training, and social media analysis. However, current methods for speech emotion recognition mostly rely on data-driven models, and the lack of emotional speech datasets significantly hinders their progress. To address this issue, this paper proposes an emotional speech dataset consisting of 6,447 audio clips for emotion recognition and analysis. The dataset includes five emotional categories: happy, sad, surprised, fearful, and angry. In addition, four speech signal processing algorithms including Fast Fourier Transform (FFT), Short-Time Fourier Transform (STFT), Mel-Frequency Cepstral Coefficients (MFCC), and Continuous wavelet Transform (CWT) are utilized for feature extraction, resulting in the creation of four emotional feature image datasets. Furthermore, eleven state-of-the-art (SOTA) classification models are selected to establish a benchmark. Experimental results show that most classification models achieve accurate recognition performance on the proposed emotional speech dataset. It is believed that the proposed dataset and benchmark can promote the research and development of emotion recognition and analysis.

关键词： Training Emotion recognition Fast Fourier transforms signal processing algorithms Speech recognition Benchmark testing signal processing Feature extraction Speech processing Research and development

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Fiber-Optic Sensor Spectrum Noise Reduction Based on a Generative Adversarial Network

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SENSORS 2024年第22期24卷 7127页

作者： Lu, Yujie Du, Qingbin Zhang, Ruijia Wang, Bo Liu, Zigeng Tang, Qizhe Dai, Pan Fan, Xiangxiang Huang, Chun Huzhou Univ Sch Informat Engn Huzhou 313000 Peoples R China Harbin Engn Univ Acoust Sci & Technol Lab Harbin 150001 Peoples R China

In the field of fiber-optic sensing, effectively reducing the noise of sensing spectra and achieving a high signal-to-noise ratio (SNR) has consistently been a focal point of research. This study proposes a deep-learning-based denoising method for fiber-optic sensors, which involves pre-processing the sensor spectrum into a 2D image and training with a cycle-consistent generative adversarial network (Cycle-GAN) model. The pre-trained algorithm demonstrates the ability to effectively denoise various spectrum types and noise profiles. This study evaluates the denoising performance of simulated spectra obtained from four different types of fiber-optic sensors: fiber Fabry-Perot interferometer (FPI), regular fiber Bragg grating (FBG), chirped FBG, and FBG pair. Compared to traditional denoising algorithms such as wavelet transform (WT) and empirical mode decomposition (EMD), the proposed method achieves an SNR improvement of up to 13.71 dB, an RMSE that is up to three times smaller, and a minimum correlation coefficient (R2) of no less than 99.70% with the original high-SNR signals. Additionally, the proposed algorithm was tested for multimode noise reduction, demonstrating an excellent linearity in temperature response with a R2 of 99.95% for its linear fitting and 99.74% for the temperature response obtained from single-mode fiber sensors. The proposed denoising approach effectively reduces the impact of various noises from the sensing system, enhancing the practicality of fiber-optic sensing, especially for specialized fiber applications in research and industrial domains.

关键词： fiber-optic sensing generative adversarial network noise reduction signal processing

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image Fusion of Noisy images Based on Simultaneous Empirical wavelet Transform

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TRAITEMENT DU signal 2020年第5期37卷 703-710页

作者： Rahmani, Akram Isvand Almasi, Mohammad Saleh, Nastaran Katouli, Moosa Arak Univ Comp Software Engn Arak *** Iran Univ Polytech Catalonia Dept Engn Math Barcelona 08028 Spain Univ South Tehran Dept Lab Fac Engn Tehran *** Iran Islamic Azad Univ Comp Software Engn Gorgan Branch Tehran *** Iran

Fusion image is the method of extracting the relevant information from two or more identical input images into one scene and creating a new image. This method allows the new image to provide comprehensive information about the wand, leading to a visual understanding of the human being. Fusion image application in image processing is an important issue. applications in many fields such as photography, microscopy, astronomy, medical imaging, satellite imagery, machine vision, biology are monitored. In this study first, an image fusion method, suggested recently based on transform empirical wavelet, was implemented in which coefficients were obtained by processing the input images. Then they were combined by applying the rules. The aim of this study is to investigate the noise effect and to remove the noise in the aforementioned suggested method. First, the noise was added to the images, and the images were decomposed into layers or coefficients. Second, by thresholding the coefficients, the noise was removed. Then the coefficients were combined based on the rules to obtain the final coefficients. In the end, the final coefficients were used to obtain the fused image. The results show that the noise removal of images during image fusion is much better and more effective than denoising before image fusion, and the demonstration of the method is proved by obtaining better results in comparison to some existing methods.

关键词： simultaneous empirical wavelet transform merge rules coefficients layers

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FAVER: Blind quality prediction of variable frame rate videos

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signal processing-image COMMUNICATION 2024年 122卷

作者： Zheng, Qi Tu, Zhengzhong Madhusudana, Pavan C. Zeng, Xiaoyang Bovik, Alan C. Fan, Yibo Fudan Univ Coll Microelect State Key Lab ASIC & Syst Shanghai 200000 Peoples R China Univ Texas Austin Dept Elect & Comp Engn Lab Image & Video Engn LIVE Austin TX 78712 USA

Video quality assessment (VQA) remains an important and challenging problem that affects many applications at the widest scales. Recent advances in mobile devices and cloud computing techniques have made it possible to capture, process, and share high resolution, high frame rate (HFR) videos across the Internet nearly instantaneously. Being able to monitor and control the quality of these streamed videos can enable the delivery of more enjoyable content and perceptually optimized rate control. Accordingly, there is a pressing need to develop VQA models that can be deployed at enormous scales. While some recent effects have been applied to full -reference (FR) analysis of variable frame rate and HFR video quality, the development of no -reference (NR) VQA algorithms targeting frame rate variations has been little studied. Here, we propose a first -of -akind blind VQA model for evaluating HFR videos, which we dub the Framerate-Aware Video Evaluator w/o Reference (FAVER). FAVER uses extended models of spatial natural scene statistics that encompass space- time wavelet -decomposed video signals, and leverages the advantages of the deep neural network to provide motion perception, to conduct efficient frame rate sensitive quality prediction. Our extensive experiments on several HFR video quality datasets show that FAVER outperforms other blind VQA algorithms at a reasonable computational cost. To facilitate reproducible research and public evaluation, an implementation of FAVER is being made freely available online: https://***/uniqzheng/HFR-BVQA.

关键词： Video quality assessment High frame rate No reference/blind Temporal band-pass filter Natural scene statistics Generalized Gaussian distribution

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Gaussian Lifting for Fast Bilateral and Nonlocal Means Filtering

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IEEE TRANSACTIONS ON image processing 2020年 29卷 6082-6095页

作者： Young, Sean I. Girod, Bernd Taubman, David Stanford Univ Dept Elect Engn Informat Syst Lab Stanford CA 94305 USA Univ New South Wales Sch Elect Engn & Telecommun Sydney NSW 2052 Australia

Recently, many fast implementations of the bilateral and the nonlocal filters were proposed based on lattice and vector quantization, e.g. clustering, in higher dimensions. However, these approaches can still be inefficient owing to the complexities in the resampling process or in filtering the high-dimensional resampled signal. In contrast, simply scalar resampling the high-dimensional signal after decorrelation presents the opportunity to filter signals using multi-rate signal processing techniques. This work proposes the Gaussian lifting framework for efficient and accurate bilateral and nonlocal means filtering, appealing to the similarities between separable wavelet transforms and Gaussian pyramids. Accurately implementing the filter is important not only for image processing applications, but also for a number of recently proposed bilateral-regularized inverse problems, where the accuracy of the solutions depends ultimately on accurate filter implementations. We show that our Gaussian lifting approach filters images more accurately and efficiently across many filter scales. Adaptive lifting schemes for bilateral and nonlocal means filtering are also explored.

关键词： Kernel Lattices Pipelines Acceleration Manifolds Iron Convolution Nonlinear filtering inverse problems Gaussian pyramids wavelet transforms

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image Fusion using Multi Resolution transforms with Human Visual System 1

Image Fusion using Multi Resolution transforms with Human Vi...

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1st International Conference on Advances in Smart Sensor, signal processing and Communication Technology, ICASSCT 2021

作者： Vadhi, Radhika Swamy, Kilari Veera Department of ECE Srinivasa Institute of Engineering and Technology Cheyyeru A.P Amalapuram India Department of ECE Vasavi College of Engineering Ibrahimbagh Telangana Hyderabad India

The Quality of vision is a practical objective in the image *** of the applications of image processing procedures is image Fusion. To get the subjective vision of a image by gathering the best data from source images of a similar scene/picture and spot them in a solitary void image is the image Fusion process. A simple and versatile approaches are statistical measures, that are applicable in any kind of image/signal processing techniques. In the first step, image is decomposed using Discrete wavelet Transform (DWT). Mathematical computation of Smoothness is proposed in transform domain. This metric is used to select important information from multiple source images resulting to fused image. Further, Human Visual System (HVS) is also explored for fusion. Here all sub bands of DWT are multiplied with HVS weights. Highest response sub band is identified from various sub bands of multiple images using HVS. These sub bands are selected to get the fused image. Smoothness based fusion technique identifies the good texture information and leave the noise affected portions from the multiple source images. HVS based fusion technique identifies the visually important information from the multiple source images. The registered multi-focus and medical images are considered as source images. The experimental results shows that proposed fusion techniques are good in terms of popular fusion metrics. © Published under licence by IOP Publishing Ltd.

关键词： Discrete wavelet transforms

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Quasi-analytic directional wavelet packets: applications to image processing

Quasi-analytic directional wavelet packets: applications to ...

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International Conference on Sampling Theory and applications (SampTA)

作者： Amir Z. Averbuch Valery A. Zheludev Sch. of Computer Science Tel Aviv University Tel Aviv Israel

Recently, a versatile library of quasi-analytic complex-valued wavelet packets (WPs) which originate from splines of arbitrary orders, was designed [1]. The real parts of the quasi-analytic WPs (qWPs) are the regular spline-based orthonormal WPs. The imaginary parts, which are slightly modified Hilbert transforms of the real parts, are the so-called complementary orthonormal WPs, which, unlike the symmetric regular WPs, are antisymmetric. Both regular and complementary WPs are well localized in time domain and their DFT spectra provide a variety of refined splits of the frequency domain. The waveforms can have arbitrary number of vanishing *** products of 1D quasi-analytic WPs (qWPs) provide a diversity of 2D waveforms oriented in multiple directions. The designed computational scheme enables us to get fast and easy implementation of the qWP transforms. The shapes of real and imaginary parts of the qWPs can be regarded as directional cosine waves with different frequencies modulated by localized low-frequency signals. For example, the set of the fourth-level WPs comprises waveforms which are oriented in 314 different directions and are oscillating with 256 different frequencies. Various combinations of qWPs form multiple frames in the 2D signal *** combination of the exceptional properties of the designed qWPs, such as unlimited directionality and oscillating structure of the waveforms, vanishing moments and refined frequency resolution, make them a powerful tool for image processing applications. The algorithms based on the qWPs proved to be competitive with the best existing methods in solving such classical image processing problems as denoising, inpainting and deblurring. The qWP algorithms are especially efficient for capturing edges and fine texture and oscillating patterns even in severely degraded *** to the above properties and next to unlimited diversity of testing waveforms, the qWPs have strong capabilities for extraction characte

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EVALUATION OF THE EFFECTS OF LUNGS CHEST X-RAY image FUSION WITH ITS wavelet SCATTERING TRANSFORM COEFFICIENTS ON THE CONVENTIONAL NEURAL NETWORK CLASSIFIER ACCURACY DURING THE COVID-19 DISEASE

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BIOMEDICAL ENGINEERING-applications BASIS COMMUNICATIONS 2023年第5期35卷

作者： Arvanaghi, Roghayyeh Meshgini, Saeed Univ Tabriz Fac Elect & Comp Engn Tabriz Iran

Background and Objective: Regarding the Coronavirus disease-2019 (COVID-19) pandemic in past years and using medical images to detect it, the image processing of the lungs and enhancement of its quality are some of the challenges in the medical image processing field. As it sounds from previous studies, the lung image processing has been raised in the other lung diseases such as lung cancer, too. Thus, the accurate classifying between normal lung image and abnormal is a challenge to aid ***: In this paper, we have proposed an image fusion technique to increase the accuracy of classifier. In this technique, some signal preprocessing tools like discrete wavelet transform (DWT), wavelet scattering transform (WST), and image fusion by using DWT are employed to enhance ordinary convolutional neural network (CNN) classifier ***: Unlike other studies, in this paper, different aspects of an image are fused with itself to emphasize its information which may be neglected in a total assessment of the image. We have achieved 89.8% accuracy for very simple structure of CNN classifier without using proposed fusion, and when we used proposed methods, the classifier accuracy increased to 91.8%.Conclusions: This study reveals using efficient preprocessing and presenting input images which lead to decrease the complications of deep learning classifier, and increase its accuracy overall.

关键词： Discrete wavelet transform (DWT) wavelet scattering transform (WST) Fusion Classification X-ray Convolutional neural network (CNN) COVID-19

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Novel Fractional wavelet Packet Transform: Theory, Implementation, and applications

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IEEE TRANSACTIONS ON signal processing 2020年 68卷 4041-4054页

作者： Shi, Jun Liu, Xiaoping Xiang, Wei Han, Mo Zhang, Qinyu Harbin Inst Technol Commun Res Ctr Harbin 150001 Peoples R China Beijing Inst ofTechnol Sch Informat & Elect Beijing Key Lab Fract Signals & Syst Beijing 100081 Peoples R China La Trobe Univ Sch Engn & Math Sci Melbourne Vic 3086 Australia Northeastern Univ Cognit Syst Lab Boston MA 02115 USA Harbin Inst Technol Shenzhen Grad Sch Shenzhen 518055 Peoples R China

The fractional wavelet transform (FRWT), which generalizes the classical wavelet transform and the well-known fractional Fourier transform, has recently been demonstrated as a powerful analytical tool for signal and image processing. However, this transform suffers from a relatively poor resolution in the high fractional frequency region, which results in difficulties in discriminating signals containing close high fractional frequency components. A simple but effective method to overcome this deficiency is the fractional wavelet packet transform (FRWPT). There exist several different definitions of the FRWPT in the literature. Unfortunately, these existing definitions do not generalize well the classical results for the conventional wavelet packet transform. The objective of this paper is to obtain a novel FRWPT that preserves the properties of its conventional counterpart. We first define the novel FRWPT and then discuss its related properties. Fractional wavelet packet subspaces are also constructed. Moreover, a recursive algorithm for implementing the proposed FRWPT is presented. Finally, we discuss potential applications of the proposed FRWPT.

关键词： Fractional Fourier transform fractional wavelet transform fractional wavelet packet transform filter bank

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