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5th IEEE International Conference on Recent Trends in Electronics, Information and Communication Technology (RTEICT)

作者： Sushmitha, M. Chetan, S. Sarkar, Sayantam Dr Ambedkar Inst Technol Dept Elect & Commun Engn VLSI Design & Embedded Syst Bengaluru India Dr Ambedkar Inst Technol Dept Elect & Commun Engn Bengaluru India Vijaya Vittala Inst Technol Dept Elect & Commun Engn Bengaluru India

ISBN: (纸本)9781728197722

There are various Discrete wavelet Transform architectures that are designed to fulfil certain requirements and criteria's. The convolution method which is an old traditional method which requires more multipliers, hardware resources and huge memory storage which is not apt to yield high speed and efficient image processing, signal processing application designs when compared to lifting method. In this paper, we have proposed an architecture for lifting scheme based CDF-5/3 2D-DWT, which includes less mathematical computations and high speed is achieved compared to existing works. The proposed architecture design is multiplier-less and utilizes lesser number of components hence it is area efficient and also achieves high throughput rate. The RTL of this design is described in VHDL language and is synthesized in Xilinx 14.5 and target device is Virtex-5 series XC5VLX110T for testing the resulting parameters like LUTs, flip- flops and slices. Higher PSNR values and lesser MSE values are achieved when tested for different kind of images. The maximum operating frequency achieved for the designed 2D-DWT architecture is 258.358MHz. Hence the proposed 2D-DWT architecture has increased speed, less computational complexity and good hardware utilization when compared to other existing works.

关键词： Lifting scheme CDF-5/3 wavelet Filter DWT 1D-DWT 2D-DWT

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Natural image Deblurring using Recursive Deep Convolutional Neural Network (R-DbCNN) and Second-Generation wavelets

Natural Image Deblurring using Recursive Deep Convolutional ...

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IEEE International Conference on signal and image processing applications (IEEE ICSIPA)

作者： Khmag, Asem Kamarudin, Noraziahtulhidayu Univ Zawia Dept Comp Syst Engn Fac Engn Az Zawia Libya Univ Coll Technol Sarawak Sch Comp & Creat Sarawak Malaysia

ISBN: (纸本)9781728133775

Deconvolution in blind digital images is a common issue in image enhancement techniques, which basically was a notion of many researches. In this study, spatial varying blind deconvloution is stated and implemented. In addition, image noise removal approach which utilizes the normalized platform of second-generation wavelet transform is applied as pre-processing step. The low and high frequencies are decomposed in this step in order to be extracted. Practically, the main merit of wavelet transform is its efficiency in reduction of data redundancy in digital images. This feature helps a lot in terms of data classification where it is easy to distinguish the signal from its noisy counterpart. The second step, a recursive deep convolutional neural network (R-DbCNN) is implemented to suppress any image blur affected by second-generation wavelet transform to further remove the blur of noisy image. The experimental results depict that the suggested method outperforms recent blur removal techniques for different bluer image types in terms of image quality and time consumption.

关键词： image deblurring second-generation wavelet transforms image enhancement convolution neural network image denoising

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A Hybrid Multi-focus image Fusion Technique using SWT and PCA 10

A Hybrid Multi-focus Image Fusion Technique using SWT and PC...

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10th International Conference on Cloud Computing, Data Science and Engineering (Confluence)

作者： Tyagi, Tushar Gupta, Parth Singh, Prabhishek Amity Univ Uttar Pradesh Amity Sch Engn & Technol Dept CSE Noida India

ISBN: (纸本)9781728127910

This paper presents a new hybrid and parallel processing image fusion technique for multi-focus images. Here, two different methods are used i.e. Stationary wavelet Transform (SWT) and Principal Component Analysis (PCA) that are implemented on the input images in parallel. These two methods are applied on same input dataset. This method is although computationally bit slower than the compared method but still it shows better results. The fused images obtained from the SWT and PCA are later again fused using PCA method. This is a parallel processing technique. The result of proposed method is compared with other traditional and conventional methods like DWT, SWT and PCA. it is observed that the result of proposed method is better than the compared methods. The result of the proposed method is analyzed qualitatively (visual appearance) and quantitatively using CC (Correlation Coefficient), UIQI (Universal image Quality Index), and PSNR (Peak signal-to-Noise Ratio). The proposed technique will have the capability to be implemented in real time applications of Visual Sensor Network (VSN).

关键词： image fusion SWT PCA VSN

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An efficient wavelet and curvelet-based PET image denoising technique

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MEDICAL & BIOLOGICAL ENGINEERING & COMPUTING 2019年第12期57卷 2567-2598页

作者： Bal, Abhishek Banerjee, Minakshi Sharma, Punit Maitra, Mausumi RCC Inst Informat Technol Kolkata India RCC Inst Informat Technol Dept Comp Sci & Engn Kolkata India Apollo Gleneagles Hosp Kolkata India Govt Coll Engn & Ceram Technol Dept Informat Technol Kolkata India

Positron emission tomography (PET) image denoising is a challenging task due to the presence of noise and low spatial resolution compared with other imaging techniques such as magnetic resonance imaging (MRI) and computed tomography (CT). PET image noise can hamper further processing and analysis, such as segmentation and disease screening. The wavelet transform-based techniques have often been proposed for PET image denoising to handle isotropic (smooth details) features. The curvelet transform-based PET image denoising techniques have the ability to handle multi-scale and multi-directional properties such as edges and curves (anisotropic features) as compared with wavelet transform-based denoising techniques. The wavelet denoising method is not optimal for anisotropic features, whereas the curvelet denoising method sometimes has difficulty in handling isotropic features. In order to handle the weaknesses of individual wavelet and curvelet-based methods, the present research proposes an efficient PET image denoising technique based on the combination of wavelet and curvelet transforms, along with a new adaptive threshold selection to threshold the wavelet coefficients in each subband (except last level low pass (LL) residual). The proposed threshold utilizes the advantages of adaptive threshold taken from BayesShrink along with the neighborhood window concept. The present method was tested on both simulated phantom and clinical PET datasets. Experimental results show that our method has achieved better results than the existing methods such as VisuShrink, BayesShrink, NeighShrink, ModineighShrink, curvelet, and an existing wavelet curvelet-based method with respect to different noise measurement metrics, such as mean squared error (MSE), signal-to-noise ratio (SNR), peak signal-to-noise ratio (PSNR), and image quality index (IQI). Furthermore, notable performance is achieved in the case of medical applications such as gray matter segmentation and precise tumor regi

关键词： image denoising PET wavelet Curvelet Thresholding Neighborhood coefficients

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Design of Digital Filters for Medical images Using Optimized Learning Based Multi-Level Discrete wavelet Cascaded Convolutional Neural Network

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Journal of Harbin Institute of Technology(New series) 2025年第2期 55-64页

作者： Vaibhav Jain Ashutosh Datar Yogendra Kumar Jain Department of Electronics and Instrumentation Engineering Rajiv Gandhi Proudyogiki Vishwavidhyalaya Department of Electronics Engineering Samrat Ashok Technological Institute

In digital signal processing, image enhancement or image denoising are challenging task to preserve pixel quality. There are several approaches from conventional to deep learning that are used to resolve such issues. But they still face challenges in terms of computational requirements, overfitting and generalization issues, etc. To resolve such issues, optimization algorithms provide greater control and transparency in designing digital filters for image enhancement and denoising. Therefore, this paper presented a novel denoising approach for medical applications using an Optimized Learning-based Multi-level discrete wavelet Cascaded Convolutional Neural Network(OLMWCNN). In this approach, the optimal filter parameters are identified to preserve the image quality after denoising. The performance and efficiency of the OLMWCNN filter are evaluated, demonstrating significant progress in denoising medical images while overcoming the limitations of conventional methods.

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A Framework For Single image Dehazing Using DWT Based Cross Bilateral Filter Fusion of Generative and ASM Models

A Framework For Single Image Dehazing Using DWT Based Cross ...

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International Conference on Innovative Sustainable Computational Technologies (CISCT)

作者： Banala Revanth Sanjay K. Dwivedi Manoj Kumar Department of Computer Science Babasaheb Bhimrao Ambedkar University Lucknow India

ISBN: (纸本)9781665474177

Due to the latest advancements in technologies like artificial intelligence, machine learning, and deep learning in image processing, computer vision, and their uses in various applications, there has been a lot of interest in the restoration of murky images. This paper gives a novel single-image dehazing (SID) framework for the restoration of single hazy image (SHI). The framework has three parts. The first part of the framework comprises three networks. A white-balanced auto-encoder network in the front, followed by a pair of sequential auto-encoder networks at the end for image dehazing (ID). A white balance auto-encoder network is used for correcting the white balance error in the input hazy image (HI). Pair of sequential auto- encoder networks gives haze-reduced output by taking corrected white balanced error image as input. The second part comprises an ASM-based model Dark Channel Prior (DCP) for ID. DCP produce haze-free images (HFI) by taking HI as input. The third part presents the fusion model for integrating the first and second parts of the framework. DWT-based cross bilateral filter model is the fusion model (FM) to get the final HFI. The first part of the framework is trained by using a hybrid loss function (perceptual loss function combined with MSE loss function) on O- HAZE, I-HAZE, and MRFID data sets. The proposed framework got considerably good result in terms of Structural Similarity Index (SSIM), Peak signal-to-noise ratio (PSNR), compared with cutting-edge methods.

关键词： Training Measurement Learning systems PSNR Benchmark testing Discrete wavelet transforms image restoration

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Improving Occluded Face Recognition with image Fusion 13

Improving Occluded Face Recognition with Image Fusion

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13th International Congress on image and signal processing, BioMedical Engineering and Informatics (CISP-BMEI)

作者： Chen, Panxi Li, Xiaoqiang Wang, Wenfeng Illinois Wesleyan Univ Bloomington IL 61701 USA Fudan Univ Shanghai Peoples R China Shanghai Inst Technol Sch Electr & Electr Engn Shanghai Peoples R China

ISBN: (纸本)9780738105451

The application of vision technology has penetrated into many fields such as tourism, education, government affairs, housing, Internet applications, etc. Among them, face recognition technology has been closely related to our lives, such as mobile phone unlocking, attendance check-in, residential access control, etc., resulting in an increasing demand for high success rate face recognition. The recent new crown virus epidemic has caused many people to wear masks to go out, and the masks covering the face will reduce the effective recognition rate. In order to improve the inconvenience caused by masks to the application of face recognition technology, we try to apply image fusion technology to improve the success rate of face recognition. We compared the recognition rates of six common image fusion technologies. These technologies use the Labeled Faces in the Wild database as the data source and the Iterative Weighted Regular Robust Coding (IR3C) algorithm as the evaluation method. The final results show that the use of these image fusion technologies can greatly improve the recognition success rate of images occluded by masks. In particular, the weighted average technique and principal component analysis give a stable performance on recognition accuracy of 99.6% and 99.0% in a relatively small-scale data set, while the LFW simulated masked face dataset provided by Wuhan University that did not perform image fusion Only has a recognition rate of 85.4%. The application of image fusion can solve some limitations in the use of face recognition and bring more convenience to various fields.

关键词： image Fusion Face Recognition Principal Component Analysis Weighted Average Discrete wavelet Transform Pyramid Fusion

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Deep learning for wireless modulation classification based on discrete wavelet transform

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INTERNATIONAL JOURNAL OF COMMUNICATION SYSTEMS 2021年第18期34卷 e4980-e4980页

作者： Al-Makhlasawy, Rasha M. Ghanem, Hanan S. Kassem, Hossam M. Elsabrouty, Maha Hamed, Hesham F. A. Abd El-Samie, Fathi E. Salama, Gerges M. Elect Res Inst Dept Comp & Syst Cairo Egypt Minia Univ Dept Elect Engn Fac Engn Al Minya Egypt Tanta Univ Dept Elect Engn & Elect Commun Fac Engn Tanta Egypt Egypt Japan Univ Sci & Technol Dept Elect & Commun Engn Alexandria Egypt Menoufia Univ Fac Elect Engn Dept Elect & Elect Commun Engn Menoufia Egypt

In the presence of noise in communication systems, constellation diagram points are scattered to the extent that may make the modulation classification a difficult task. With the plethora of applications of machine and deep learning, several communication systems have adopted machine and deep learning to solve some classical detection and classification problems. Casting the modulation order detection as a pattern classification of the constellation images opens the door for application of mature machine learning and image processing tools to solve the classification problem, efficiently. This paper presents a system based on a wavelet-aided convolutional neural network (CNN) classifier to efficiently detect the modulation type and order in the presence of noise. The proposed system depends on a pretrained CNN setup, which is trained with a set of constellation diagrams for each modulation scheme and used after that for testing. In addition, discrete wavelet transform (DWT) is investigated to generate representative patterns from constellation diagrams to be used for the training and testing tasks as well. The wavelet approximation images and their corresponding wavelet sub-bands across all predefined scales are used in the dataset. Several pretrained networks including AlexNet, VGG-16, and VGG-19 are used as classifiers for the modulation type from the DWTs for different constellation diagrams. Several simulation experiments are presented in this paper to compare different scenarios for modulation classification at different signal-to-noise ratios (SNRs).

关键词： convolutional neural networks (CNNs) deep learning (DL) discrete wavelet transform (DWT) modulation classification pretrained networks

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Simultaneous Reconstruction and Denoising of Extremely Sparse 5-D Seismic Data by a Simple and Effective Method

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2022年 60卷 1页

作者： Wang, Hang Chen, Yunfeng Oboue, Yapo Abole Serge Innocent Abma, Ray Geng, Zhicheng Fomel, Sergey Chen, Yangkang Zhejiang Univ Key Lab Geosci Big Data & Deep Resource Zhejiang Sch Earth Sci Hangzhou 310027 Zhejiang Peoples R China Univ Texas Austin John A & Katherine G Jackson Sch Geosci Bur Econ Geol Austin TX 78712 USA

5-D data is the original recorded form in the 3-D seismic acquisition, which includes sufficient information from all five dimensions. However, environmental and economic logistic difficulties often severely impact the data acquisition geometry, leading to raw data with missing traces and strong contaminating random noise. This deficiency often causes troubles in subsequent processing. Thus, an efficient interpolation and denoising method is required to recover useful signals. Unfortunately, practical applications of many existing reconstruction algorithms are limited by their intensive computational cost when applied to the 5-D data. Additionally, the stability of these algorithms is also challenged by complex geological structures, which often degrades the reconstruction performance. To seek solutions to the aforementioned problems, we design a simple and effective framework for fast reconstruction and denoising of undersampled 5-D seismic data via a two-step process. First, we prepare the initial model from the original recordings by constructing a 3-D gather at each common offset point. This step effectively interpolates the missing traces in 3-D common offset gathers by exploiting the data coherency in the adjacent areas (i.e., nearby mid-points). In the second step, the processed 5-D data is reorganized into 3-D common midpoint gathers, with each of them further sorted into a 2-D section according to absolute offset values. Then a conventional 2-D processing algorithm (e.g., F-X prediction, wavelet thresholding, or multichannel singular spectrum analysis) is invoked to filter the obtained 2-D section. The proposed workflow has a low overall computational cost and preserves signal fidelity. We use this framework to simultaneously denoise and interpolate the low-quality and extremely sparse seismic data. The synthetic and field examples both demonstrate the superb performance of the proposed framework in comparison with conventional methods.

关键词： Noise reduction image reconstruction Three-dimensional displays Data models Transforms Geology Noise measurement 5-D fast reconstruction sparse

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Deep learning model for real-time image compression in Internet of Underwater Things (IoUT)

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JOURNAL OF REAL-TIME image processing 2020年第6期17卷 2097-2111页

作者： Krishnaraj, N. Elhoseny, Mohamed Thenmozhi, M. Selim, Mahmoud M. Shankar, K. SASI Inst Technol & Engn Dept Comp Sci & Engn Tadepalligudem Andhra Prades India Mansoura Univ Fac Comp & Informat Mansoura Egypt SRM Inst Sci & Technol Dept IT Kanchipuram Tamil Nadu India Prince Sattam Bin Abdulaziz Univ Al Aflaj Coll Sci & Human Studies Dept Math Al Kharj Saudi Arabia Kalasalingam Acad Res & Educ Sch Comp Krishnankoil India

Recently, the advancements of Internet-of-Things (IoT) have expanded its application in underwater environment which leads to the development of a new field of Internet of Underwater Things (IoUT). It offers a broader view of applications such as atmosphere observation, habitat monitoring of sea animals, defense and disaster prediction. Data transmission of images captured by the smart underwater objects is very challenging due to the nature of underwater environment and necessitates an efficient image transmission strategy for IoUT. In this paper, we model and implement a discrete wavelet transform (DWT) based deep learning model for image compression in IoUT. For achieving effective compression with better reconstruction image quality, convolution neural network (CNN) is used at the encoding as well as decoding side. We validate DWT-CNN model using extensive set of experimentations and depict that the presented deep learning model is superior to existing methods such as super-resolution convolutional neural networks (SRCNN), JPEG and JPEG2000 in terms of compression performance as well as reconstructed image quality. The DWT-CNN model attains an average peak signal-to-noise ratio (PSNR) of 53.961 with average space saving (SS) of 79.7038%.

关键词： Deep learning image compression IoUT Underwater image reconstruction

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