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Determining the number of tetrominoe orders for denoising applications performed by Tetrolet transform

Determining the number of tetrominoe orders for denoising ap...

引用

IEEE signal processing and Communications applications (SIU)

作者： Murat Ceylan Ayşe Elif Öztürk Elektrik - Elektronik Mühendisliği Bölümü Selçuk Üniversitesi Konya Türkiye

While Tetrolet transform (TT) which is one of the multi-resolution analysis improved recently is applied to images, 5 different shape called tetrominoes is gathered for 4×4 pixel blocks and TT is performed by arranging pixels according to this order. Tetrominoes can be chosen with 117 different combinations. In this study, firstly benchmark images (Lena, Barbara, Boat, Mandrill, Cameraman) and liver MR (magnetic resonance) images are denoised by utilizing TT with 1 to 117 different tetrominoe combinations respectively and optimal number of orders is determined for different noise rates by comparing obtained PSNR (peak signal to noise ratio) results. After that, images are denoised by using just optimal number of tetrominoe orders and all 117 combinations seperately. The operation times are compared. It is clearly seen that using all possible combinations for denoising causes redundant processes and an optimal number of tetrminoe orders can be specified for different image sets according to noise ratio. Decreasing number of combinations shortened the operation time seriously.

关键词： PSNR Noise reduction conferences Discrete wavelet transforms

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wavelet based SAR data denoising and analysis

Wavelet based SAR data denoising and analysis

引用

IEEE International Advance Computing conference, IACC

作者： Arundhati Misra B Kartikeyan S Garg ATDG EPSA SAC/ISRO Ahmedabad India IAQD SIPA SAC/ISRO Ahmedabad India Department Of Computer Science Nirma University Ahmedabad India

ISBN: (纸本)9781479925735

Real world signals are afflicted by noise, be it the communication signals or the remote sensor signals including the medical imaging sensors or surveillance sensors. In the case of signals from SAR(Synthetic Aperture Radar) sensor from remote sensing platforms, or USG or MRI from medical images, the noise is multiplicative. The coherent nature of the signal processing involved in the data generation for the latter, gives rise to a g rainy kind of signal dependent noise called speckle. Speckle noise affects the radiometric quality of the image and makes data interpretation and analysis difficult. Since the utility of any data depends on the accuracy of the data both in terms of geometry and radiometry, denoising becomes mandatory for image analysis applications. The common problem with most of these denoising filters is that they blur the images while reducing the noise. The challenge thus, lies in cleaning the image without sacrificing the geometric resolution. wavelet based filtering techniques show promising results in this regard. In this paper the results and analysis of performing filtering on high resolution SAR data, using different mother wavelets of Daubechie's using soft thresholding technique, are presented.

关键词： Noise reduction Noise Filtering Spatial resolution wavelet transforms Synthetic aperture radar Speckle

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Comparative evaluation of super-resolution techniques for multi-face recognition using light-field camera

Comparative evaluation of super-resolution techniques for mu...

引用

International conference on Digital signal processing (DSP)

作者： R. Raghavendra Kiran B Raja Bian Yang Christoph Busch Norwegian Biometric Laboratory Gjøvik University College Norway

Face recognition in a wide area surveillance scenario is a challenging problem as it requires multiple cameras, needs to process low resolution samples and high computation. In this paper, we experiment with a Light-Field Camera (LFC) to effectively address a wide area surveillance scenario. The interesting property of the LFC is that, it can provide different focus (or depth) images in a single capture that was not possible with any conventional camera (CCTV or PTZ) widely employed for surveillance applications. We further explore the property of multiple depth images rendered by LFC to perform the super-resolution reconstruction. To this extent, we first present a quantitative and comparative evaluation of well known super-resolution techniques. We then, present a new resolution enhancement scheme based on Discrete wavelet Transform (DWT) as well as existing super-resolution scheme to capture the high frequency components from different depth images rendered by LFC and thereby improving the performance of the face recognition. Experiments are carried out on our new face dataset to bring out the merits and demerits of employing the light-field camera for face recognition in wide area surveillance applications.

关键词： image resolution Face Cameras Energy resolution Discrete wavelet transforms Surveillance Probes

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An Unsupervised Domain Adversarial wavelet Learning Method for registration of Optical and SAR Heterogeneous Remote Sensing images

An Unsupervised Domain Adversarial Wavelet Learning Method f...

引用

signal, Information and Data processing (ICSIDP), IEEE International conference on

作者： Hongze Ye Shenfu Zhang Liangzhi Li Xiangchao Meng School of Information Science and Engineering Ningbo University Ningbo China College of Geological Engineering and Geomatics Chang’an University Xi’an China

ISBN: (数字)9798331515669

ISBN: (纸本)9798331515676

Optical images and synthetic aperture radar (SAR) remote sensing images are highly complementary information for Earth observation and applications. Accurate registration of these two data types is essential for subsequent collaboration processing and analysis. However, traditional methods struggle to achieve effective registration due to nonlinear radiometric disparities and geometric distortions. Meanwhile, supervised deep learning approaches require costly manual annotations. To address these issues, this paper proposes an unsupervised registration method based on domain adversarial wavelet learning. We innovatively design an inter-domain adversarial mutual transfer (IAT) module to alleviate cross-modal differences while preserving structural features. Additionally, wavelet transform is employed at different scales for feature matching, to mitigate radiometric disparities and enhance feature recognizability. By incorporating an attention mechanism, the network structure is optimized through spatial alignment and image similarity. The proposed method outperforms other state-of-the-art methods in both quantitative and qualitative aspects of benchmark datasets.

关键词： Optical filters wavelet transforms wavelet domain Optical distortion Radiometry Optical imaging Optical sensors Nonlinear optics Synthetic aperture radar Remote sensing

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On the distribution of the DCT coefficients

On the distribution of the DCT coefficients

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

作者： T. Eude R. Grisel H. Cherifi R. Debrie La3i-L CIA INSA Rouen Mont Saint Aignan France

The paper presents a statistical study in order to improve the image compression rate in discrete cosine transform (DCT) based methods and especially for compression methods which are defined in JPEG norm. It is realised extensively, using a battery of parametric and non-parametric fit-tests and laws such as those of Cauchy, Laplace, Gauss, and a mixture distribution of Laplace and Gaussian. Considering the absence of results for these laws in the literature, the authors constructed adapted statistical tools for DCT coefficients analysis. Then, they show that these coefficients can be modelized by a Gaussian finite mixture distribution. They also comment on the obtained results for medical images applications and explain how the compression rate can be improve with them.< >

关键词： Discrete cosine transforms image coding Transform coding Biomedical imaging Quantization Gaussian distribution wavelet transforms Testing Batteries Pixel

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Motion-compensated orthogonal transforms for multiview video coding

Motion-compensated orthogonal transforms for multiview video...

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European signal processing conference (EUSIPCO)

作者： Markus Flierl Max Planck Center for Visual Computing and Communication Stanford University California

This paper discusses coding of multiview video with motion-compensated orthogonal transforms. These transforms have recently been introduced to generate strictly orthonormal decompositions of image sequences. Orthonormality is maintained for arbitrary motion compensation among the images. This is of particular interest for coding applications as motion compensation is inaccurate due to quantization of motion information. This is in contrast to the well known motion-compensated lifted wavelets where the properties of the decomposition are motion-dependent. This disadvantage affects compression efficiency and in particular that of multiview video signals. Multiview video coding schemes utilize motion and disparity compensation when decomposing the multiview imagery into view-temporal subbands. Usually decompositions in time and view direction are cascaded. Motion-compensated lifted wavelets suffer from their motion-dependent decomposition and in particular if cascaded for multiview video coding. On the other hand motion and disparity compensated orthogonal transforms offer the advantage that the overall decomposition is still strictly orthonormal. This paper investigates the advantages of strict orthonormality that is offered by motion-compensated orthogonal transforms.

关键词： Video coding Encoding Motion compensation wavelet transforms Radiation detectors image sequences

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Multiresolutional encoding and decoding in embedded image and video coders

Multiresolutional encoding and decoding in embedded image an...

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

作者： Zixiang Xiong Beong-Jo Kim W.A. Pearlman Department of Electrical Engineering University of Hawai Honolulu HI USA Center for Image Processing Research Rensselaer Polytechnic Institute Troy NY USA

We address multiresolutional encoding and decoding within the embedded zerotree wavelet (EZW) framework for both images and video. By varying a resolution parameter, one can obtain decoded images at different resolutions from one single encoded bitstream, which is already rate scalable for EZW coders. Similarly one can decode video sequences at different rates and different spatial and temporal resolutions from one bitstream. Furthermore, a layered bitstream can be generated with multiresolutional encoding, from which the higher resolution layers can be used to increase the spatial/temporal resolution of the images/video obtained from the low resolution layer. In other words, we have achieved full scalability in rate and partial scalability in space and time. This added spatial/temporal scalability is significant for emerging multimedia applications such as fast decoding, image/video database browsing, telemedicine, multipoint video conferencing, and distance learning.

关键词： image resolution image coding Decoding Spatial resolution Videoconference Scalability Multimedia databases Video sequences image databases Spatial databases

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Optimizing Hybrid Sparse and Block-Based Compressed Sensing: A Performance Study for Biomedical Imaging and Video Surveillance applications

Optimizing Hybrid Sparse and Block-Based Compressed Sensing:...

引用

Innovations in High Speed Communication and signal processing (IHCSP), International conference on

作者： Nitish Vashisht M. Sudhakara Reddy Gajanan H Chavhan P. K. Miniappan Praney Madan Venkatasubramanian D Chitkara Centre for Research and Development Chitkara University Himachal Pradesh India School of Sciences Physics & Electronics JAIN (Deemed to be University) Bangalore Karnataka E&TC Vishwakarma Institute of Technology Pune India Department of Mechanical Engineering Karpagam Academy of Higher Education Coimbatore India Centre of Research Impact and Outcome Chitkara University Rajpura Punjab India Department of IT Prince Shri Venkateshwara Padmavathy Engineering College Chennai

ISBN: (数字)9798350368949

ISBN: (纸本)9798350368956

One of the most promising fields, compressed sensing has found a home in video streaming applications, where it seeks to compress or sparsely represent signals by reducing their dimensions. The sparse representation is a good approximation to the signal since the amplitude of the coefficient’s decays according to the power law. signals are projected into low-dimensional space to generate measurement vectors. A sparse signal may be accurately rebuilt using the measurement vector. signals or noise that are not sparse but can be compressed and might result in an approximation. images in the frequency or wavelet domain are ideal for Compressed Sensing because they are compressible by nature. A more precise reconstruction of the original picture is possible due to an improvement in reconstructed image quality and a reduction in the compression ratio, which is the ratio of the dimensions of the number of pixels to the measurement *** Sparse and Block-based Compressed Sensing Algorithm (HSBCA) is the name of the approach that merged sparse-based and block-based compressed sensing algorithms. Block Compressed Sensing, the first module of HSSBCA, processes each message by dividing it into numerous picture regions, which results in decreased run time. In contrast, performance drops while picture reconstruction is underway; to remedy this, the research employs the second module, the sparse compressed sensing method, to boost reconstruction efficiency.

关键词： image coding wavelet domain Sparse approximation Magnetic resonance imaging signal processing algorithms Biomedical measurement Streaming media Vectors image reconstruction Compressed sensing

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Efficient medical image transformation method for lossless compression by considering real time applications

Efficient medical image transformation method for lossless c...

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International conference on signal processing and Communication Systems, ICSPCS

作者： Farshid Sepehrband Mohammad Mortazavi Seyed Ghorshi Jeiran Choupan School of Science and Engineering Sharif University of Technology Iran

Medical images contain human body pictures and used widely in diagnosis and surgical purposes. Compression is needed for medical images for some applications such as profiling patient's data or transmission systems Due to the importance of the information of medical images, lossless or visually lossless compression preferred. Lossless compression mainly consists of transformation and encoding steps. On the other hand, hardware implementation of lossless compression algorithm accelerates real time tasks such as online diagnosis and telemedicine. Lossless JPEG, JPEG-LS and lossless version of JPEG2000 are few well known methods for lossless compression. This paper is focused on the transformation step of compression and introduced a new transformation which is efficient in both entropy reduction and computational complexity. A new method is then achieved by improving the perdition model which is used in lossless JPEG. Our new transformation increases the energy compaction of prediction model and as a result reduces entropy value of transformed image. However, our new method is low complex. After a mathematical proof for efficiency of the new method, it is applied to more than hundreds of test-cases and the results are compared with previous methods and it shows about 8 percent improvement in average. As a result, the new algorithm shows a better efficiency for transforming lossless medical images, especially for online applications.

关键词： image coding Transform coding Entropy Compaction Biomedical imaging Pixel Discrete wavelet transforms

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A HIGH-PERFORMANCE ARCHITECTURE OF JPEG2000 ENCODER

A HIGH-PERFORMANCE ARCHITECTURE OF JPEG2000 ENCODER

引用

European signal processing conference

作者： Damian Modrzyk Michal Staworko Institute of Telecommunications Warsaw University of Technology

This article presents hardware architecture of JPEG2000 encoder core, oriented for HD video broadcast and surveillance applications. Thanks to developed efficient 2-D DWT engine that is capable of computing four coefficients per clock cycle, and adopted two EBCOT TIER-1 modules, with smart switching of the channels, the maximum compression speed of 180 Msamples/s at 100 MHz, in lossy mode is achieved. The architecture is implemented in VHDL and synthesised for FPGA devices, and ASIC 0.13 μm CMOS technology. Performance simulations, conducted on a set of natural images and video sequences, have revealed that the encoder is capable of processing 1080p 4:4:4 signal with a speed of 30 frames per second. Additionally, an excellent quality of reconstructed images has been observed, with respect to the reference, software encoder.

关键词： Transform coding Discrete wavelet transforms image coding Encoding Quantization (signal) Hardware

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