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Hybrid model for preserving brightness over the digital image processing

Hybrid model for preserving brightness over the digital imag...

引用

International conference on Computer and Communication Technology (ICCCT)

作者： Vaishali Ahirwar Himanshu Yadav Anurag Jain Department of Computer Science & Engineering RITS Bhopal

Digital image processing is versatile research in this era. Many researchers implement different types of organizations like image restoration, image enhancement, color image processing, image segmentation etc. image enhancement technique is among the simplest and most appealing area of digital image processing. Enhancement techniques like brightness preservation, contrast enhancement highlight certain features means depend which part of the image want to be enhance some application some input image including noise, reduction or removal of noise is also form of image enhancement. Brightness preservation has enhanced visual quality of digital image so that the limitation contained in these images is used for various applications in a better way. A very popular technique for image enhancement is histogram equalization (HE) and curvelet transformation. HE technique is commonly employed for image enhancement because of its simplicity and comparatively better performance on almost all types of images. Another widely used technique is curvelet transformation. This technique is identified and separate bright regions of image but more error rate and low peak signal to noise ratio(PSNR), result of this technique is brightness preservation level is low and output image is gray. This paper design a hybrid model through discrete cosine transformation, discrete wavelet transformation and combine output of both techniques with image fusion. Proposed algorithm enhanced features and removal noise by decomposition of image using DWT and discrete cosine transformation, adaptive histogram equalization is very important part in this algorithm for smooth image. The tested results of different images are comparing with previous method, generating result with different parameters; less mean square error and high PSNR for improve the quality of an image. This paper presents a hybrid model used various parameter for enhance images like satellite images, medical images etc.

关键词： Discrete wavelet transforms Brightness Histograms image fusion PSNR Discrete cosine transforms

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Activity detection in unknown noise environment

Activity detection in unknown noise environment

引用

International conference on Acoustics, Speech, and signal processing (ICASSP)

作者： E. Fishler H. Messer Department of Electrical Engineering Systems Tel-Aviv University Tel-Aviv Israel

In many applications there exists an array of cells (or bins), each containing either an activity (signal) plus noise, or noise only. A common problem is to identify the active bins, assuming that the noise level in the array is unknown. In this paper we present a novel approach for solving this problem. The approach is based on two steps. In the first, we estimate the noise level and in the second we perform a sequential test to decide, for each bin, whether it is active or not. We show that the proposed algorithm collapses to well known special cases. The performance of the proposed algorithm is analyzed analytically and is demonstrated via simulation results.

关键词： Working environment noise Noise measurement Noise level wavelet transforms Performance analysis Additive noise image denoising Performance evaluation Sequential analysis Analytical models

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Medical image Compression Using Ripplet Transform

Medical Image Compression Using Ripplet Transform

引用

International conference on Intelligent Computing applications (ICICA)

作者： C. Dhaarani D. Venugopal A. Sivanantha Raja Dept of ECE K.L.N College of Information Technology Madurai Tamilnadu India Dept of ECE A. C. College of Engineering & Technology Karaikudi Tamilnadu India

Now-a-days the medical image compression plays a major role in the image processing. Even though the technology is improvised but still it needs the storage space and efficient bandwidth utilization. This paper presents the compression of grey scale medical images. The proposed method uses a new transform called Ripplet Transform, a higher dimensional generalization of the curvelet transform used to represent the images or two dimensional signals at different scales and different directions and Huffman algorithm to encode significant coefficients. The main goal of this method is to improve the compression ratio and to minimize the mean square error. From the experimental results obtained it is proved that the compression ratio and the peak to signal noise ratio is achieved high for various Medical images.

关键词： image coding Biomedical imaging PSNR wavelet transforms Huffman coding Magnetic resonance imaging

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Digital image watermarking scheme based on histogram in DWT domain

Digital image watermarking scheme based on histogram in DWT ...

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International conference on Digital Content, Multimedia Technology and its applications (IDC)

作者： Xiao-Chen Yuan Chi-Man Pun Department of Computer and Information Science University of Macau Macao China

A robust and geometric invariant digital watermarking scheme for gray-level images is proposed in this paper. The scheme carries out watermark embedding and extraction based on histogram in DWT domain. For watermark embedding, the original image is decomposed into the approximation and details sub-bands. Pixels of the approximation sub-band are grouped into m blocks, each of which has the same number of intensity-levels, thus the block histogram is generated; with the block histogram, pixels are moved to form a specific pattern in the intensity-level histogram distribution, indicating the watermark. For watermark extraction, the watermarked image is decomposed into the approximation and details sub-bands; then the pixels in the approximation sub-band are grouped into blocks in the similar manner. According to the histogram distribution in each block, the watermark is extracted. Experimental results show that the proposed scheme is highly robust against JPEG compression, geometric attacks and some common signal processing.

关键词： image coding Discrete wavelet transforms Fractals Robustness Cryptography Pixel Transform coding

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Utilising Machine Learning for Modulation and signal processing

Utilising Machine Learning for Modulation and Signal Process...

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Communication & Computing (WCONF), World conference on

作者： Daljeet Pal Singh Madhav Singh Solanki Renuka Jyothi S Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Department of Computer Science and Engineering Sanskriti University Mathura Uttar Pradesh India Department of Biotechnology and Genetics School of Sciences JAIN (Deemed to be University) Bangalore Karnataka India

ISBN: (数字)9798350395327

ISBN: (纸本)9798350395334

Device studying (ML) is an effective device that has been used in many packages, consisting of virtual sign processing, communications structures, and modulation reputation. In particular, ML has been extensively studied for modulation recognition, which is a hard hassle because of the always-changing and unpredictable nature of the sign. With the aid of leveraging ML strategies, which can be used to quantify and check specific houses of a signal, reliable modulation popularity and signal processing strategies can be evolved. For instance, ML can be used to automatically discover modulation class when the image shape of the signal is unknown. It is achieved via reading the sign with device learning techniques consisting of Fourier transform; wavelet-based totally function extraction, and convolutional neural networks. Different applications encompass channel estimation, signal detection, and optimization of verbal exchange networks. Further to recognizing modulation codecs, ML-based algorithms may be used to enhance signal processing and communique systems. It could be done with the aid of developing smarter systems that are capable of studying global data. ML can be used to robotically apprehend anomalous alerts and allow networks to quickly adapt to converting situations. ML techniques can also be used to optimize community parameters, which include power management, coding schemes, and modulation codecs, so as to maximize their performance.

关键词： Performance evaluation wavelet transforms Codecs Shape Modulation signal processing algorithms Feature extraction Classification algorithms Standards signal detection

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Rate scalable video coding using a foveation-based human visual system model

Rate scalable video coding using a foveation-based human vis...

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International conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Zhou Wang Ligang Lu A.C. Bovik Laboratory for Image and Video Engineering LIVE Department of Electrical and Computer Engineering University of Texas Austin Austin TX USA Video and Image Systems IBM Thomas J. Watson Research Center Yorktown Heights NY USA

Recently, there have been two interesting trends in image and video coding research. One is to use human visual system (HVS) models to improve the current state-of-the-art coding algorithms by better exploiting the properties of the intended receiver. The other is to design rate-scalable video codecs, which allow the extraction of coded visual information at continuously varying bit rates from a single compressed bitstream. We follow these two trends and propose a foveation scalable video coding (FSVC) algorithm, which supplies good quality-compression performance as well as effective rate scalability to support simple and precise bit rate control. A foveation-based HVS model plays a key role in the algorithm. The algorithm is amenable to the inclusion of various HVS models and adaptable to different video communication applications.

关键词： Video coding Humans Visual system Video compression image coding Partitioning algorithms Bit rate Scalability Bandwidth Discrete wavelet transforms

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Using image processing on MRI Scans

Using Image Processing on MRI Scans

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IEEE International conference on signal processing, Informatics, Communication and Energy Systems (SPICES)

作者： Chetan Patil M G Mathura S Madhumitha S Sumam David Merwyn Fernandes Anand Venugopal B. Unnikrishnan Dept. of E&C NITK Surathkal Mangalore 575025 India Dept. of Radiology Kasturba Medical College Mangalore 575001 India Dept. of Community Medicine Kasturba Medical College Mangalore 575001 India

Alzheimer's disease (AD) is an irreversible and progressive brain disease that gradually destroys memory and thinking skills to an extent that it starts affecting the daily life. It has become the most common cause of dementia among older people. The work presented in this paper evaluates the utility of image processing on the Magnetic Resonance Imaging (MRI) scans to estimate the possibility of an early detection of AD. The total brain atrophy and specifically the hippocampal atrophy are considered strong diagnostic tests for AD. T1 weighted MRIs have been used for the purpose of image processing to evaluate atrophy. The paper demonstrates the applications of several image processing techniques such as K-means clustering, wavelet transform, watershed algorithm and also a customized algorithm tailored for the specific case. It has been implemented on the open source platforms, OpenCV and Qt, which facilitates the implementation and utility of the developed product in the hospitals without requiring any proprietary software. The results obtained from the project could aid the analysis to detect AD along with correlation with the psychiatric results and could thus assist the doctors in detecting AD at an early stage. This could progressively help in understanding and treating AD.

关键词： Atrophy Magnetic resonance imaging Alzheimer's disease Hippocampus Medical diagnostic imaging

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Detection of Detonators in Baggage Using Deep CNN Architecture: A Comparative Study of Contrast Enhancement and Feature Extraction Methods

Detection of Detonators in Baggage Using Deep CNN Architectu...

引用

International conference on image and signal processing and their applications (ISPA)

作者： Oulhissane Lynda Merah Mostefa Simona Moldovanu Luminita Moraru Laboratory of Signals and Systems LSS Faculty of Science and Technology Abdelhamid Ibn Badis University Mostaganem Algeria MSlab Department of Computer Science and Information Technology Modelling & Simulation Laboratory Faculty of Automation Computers Electrical Engineering and Electronics Dunarea de Jos University of Galati Galati Romania MSlab Department of Chemistry Physics & Environment Modelling & Simulation Laboratory Faculty of Sciences and Environment Dunarea de Jos University of Galati Galati Romania

ISBN: (数字)9798350309249

ISBN: (纸本)9798350309256

Visual inspection of dual-energy X-ray radiographic images of cabin baggage requires high performance, but is hindered by various challenges such as low target prevalence, variability in target visibility, possible presence of multiple targets, and security personnel fatigue and inattention. Artificial intelligence (AI) techniques, particularly deep convolutional neural networks (CNNs), have shown promise in improving the automatic detection of explosives, even with low-resolution radiographic images, especially in high baggage throughput scenarios. In this paper, we focus on the detection of detonators as components of improvised explosive devices. The proposed approach involves comparing two experiments implemented in a deep CNN architecture using TensorFlow and Keras libraries. In the first experiment, raw dual-energy radiographic images without any enhancement were used. The second experiment includes three methods for contrast enhancement and feature extraction: the Contrast Limited Adaptive Histogram Equalization (CLAHE) method, the wavelet transform-based method, and the mixed CLAHE RGB-wavelet method. In the latter two methods, Haar, Db2, Coif2, and Sym2 mother wavelet functions at two levels (HH and HL) were employed. The analysis of results focuses on a comparative study of performance measures such as accuracy, precision, recall, and F1 score. It was found that the preprocessing methods used in experiment 2, for the two evaluated classes (detonator and no detonator), achieved higher accuracy compared to the raw radiographic images used in experiment 1 (98.08%). The highest accuracies in experiment 2, with a value of 100%, were obtained with the CLAHE method (green channel in grayscale, blue channel in grayscale, and RGB channels) and the wavelet transform method with Haar mother wavelet at two levels HL

关键词： wavelet transforms Radiography Visualization Gray-scale Inspection Feature extraction Libraries

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Multi-Rate signal processing with the Use of Filter Banks Composed of Parallel FIR Filters

Multi-Rate Signal Processing with the Use of Filter Banks Co...

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International conference on Microelectronics, MIEL

作者： M. Banach R. Długosz Poznan University of Technology Institute of Architecture and Spatial Planning Nieszawska 13C Poznań Poland Aptiv Services Poland ul. Podgórki Tynieckie 2 Kraków Poland

The paper presents a concept of a hardware implementation of 2-D finite impulse response (FIR) filter banks for the application in image processing and analysis. Banks composed of low-and high-pass FIR filters are basic components of multi-stage discrete wavelet transform (DWT). The applications of such solutions that are in the scope of our interests are vision systems used in automotive active safety functions (e.g in line departure warning). Basics of the DWT are broadly described in the literature. In our work we focus on solutions supporting hardware realization of filter banks for DWT. The proposed parallel and asynchronous circuits allow to achieve the processing time for a single pixel not exceeding 2 to 4 ns, depending on the size of the mask (data for TSMC 180 nm CMOS process).

关键词： Discrete wavelet transforms Finite impulse response filters Hardware Frequency response Complexity theory

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SAR image despeckling using undecimated directional filter banks and mean shift

SAR image despeckling using undecimated directional filter b...

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IEEE International conference on image processing

作者： Gong Zhang Wenhua Shi Jing Xu Ning Li Mark J. T. Smith College of Information Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing China School of Electrical and Computer Engineering Purdue University West Lafayette IN USA

The granular appearance of speckle noise in synthetic aperture radar (SAR) imagery can make it difficult to visually and automatically interpret SAR data. Speckle reduction is a prerequisite for many SAR image processing tasks. In this paper, a novel method of SAR image despeckling is presented that uses undecimated directional filter banks (UDFB) and mean shift clustering. The UDFB is obtained by manipulating the resampling matrices in the Bamberger directional filter banks (DFB), such that low computational complexity is preserved, while achieving shift invariance that could be useful in pattern recognition and image denoising applications. A nonparametric estimator of the density gradient is employed in the joint spatial-range domain of the directional bands obtained by the UDFB. Examples included at the end of the paper illustrate typical performance results obtained using this method.

关键词： Filter bank Speckle Discrete wavelet transforms Filtering image analysis Synthetic aperture radar signal analysis Channel bank filters Educational institutions Information science

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