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image-CODING USING ADAPTIVE RECURSIVE INTERPOLATIVE DPCM

IEEE TRANSACTIONS ON image processing 1995年第8期4卷 1061-1069页

作者： GIFFORD, EA HUNT, BR MARCELLIN, MW UNIV ARIZONA DEPT ELECT & COMP ENGNTUCSONAZ 85721

A predictive image coder having minimal decoder complexity is presented, The image coder utilizes recursive interpolative DPCM in conjunction with adaptive classification, entropy-constrained trellis coded quantization, and optimal rate allocation to obtain signal-to-noise ratios (SNR's) in the range of those provided by the most advanced transform coders.

关键词： image coding image reconstruction Decoding Discrete cosine transforms Quantization signal processing algorithms Pulse modulation Decorrelation wavelet transforms signal resolution

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Orthogonal Ramanujan Sums, Its Properties, and applications in Multiresolution Analysis

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IEEE TRANSACTIONS ON signal processing 2018年第21期66卷 5789-5798页

作者： Yadav, Devendra Kumar Kuldeep, Gajraj Joshi, Shiv Dutt SAG Delhi 110054 India Indian Inst Technol Delhi Dept Elect Engn New Delhi 110016 India

signal processing community has recently shown interest in Ramanujan sums, which were defined by S. Ramanujan in 1918. In this paper, we propose a family of sequences, termed here as orthogonal Ramanujan sums, C-q(k)(n) and also derive their various properties. We also prove two new properties of the Ramanujan Sums, namely, shifted multiplication property and interpolation property. These properties are used to define higher order orthogonal Ramanujan sums. These sums are periodic in nature and are orthogonal for different values of k and q. Using orthogonal Ramanujan sums, a new representation of finite length signal is given, termed here as orthogonal Ramanujan periodic transform. The coefficients obtained using this transform can be divided into two parts, first coefficient gives smoothing component and the rest give the detail components of the signal. Further, application of the orthogonal Ramanujan sums in discrete wavelet transform is developed. Construction of Ramanujan wavelet analysis matrix for applying discrete wavelet transform on a signal is described. The application of this matrix on an image of size N x N, where N is divisible by integer q will result in q(2) - 1 detail components and one average component in transform domain. Some of the applications of this transform are also demonstrated, especially in the field of image processing.

关键词： Ramanujan sums orthogonal Ramanujan sums orthogonal Ramanujan periodic transform multi resolution analysis discrete wavelet transform

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Design and performance of a pixel-level pipelined-parallel architecture for high speed wavelet-based image compression

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COMPUTERS & ELECTRICAL ENGINEERING 2005年第8期31卷 572-588页

作者： Masoudnia, A Sarbazi-Azad, H Boussakta, S IPM Inst Studies Theoret Phys & Math Sch Comp Sci Tehran Iran Sharif Univ Technol Dept Comp Engn Tehran Iran Univ Leeds Sch Elect & Elect Engn Leeds LS2 9JT W Yorkshire England

wavelets have widely been used in many signal and image processing applications. In this paper, a new serial-parallel architecture for wavelet-based image compression is introduced. It is based on a 4-tap wavelet transform, which is realised using some FIFO memory modules implementing a pixel-level pipeline architecture to compress and decompress images. The real filter calculation over 4 x 4 window blocks is done using a tree of carry save adders to ensure the high speed processing required for many applications. The details of implementing both compressor and decompressor sub-systems are given. The primarily analysis reveals that the proposed architecture, implemented using current VLSI technologies, can process a video stream in real time. (c) 2005 Elsevier Ltd. All rights reserved.

关键词： image processing compression decompression wavelet transform serial-parallel architecture pipelining FPGA implementation performance

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Efficient seismic volume compression using the lifting scheme

Efficient seismic volume compression using the lifting schem...

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Conference on wavelet applications in signal and image processing viii

作者： Khene, FM Abdul-Jauwad, SH King Fahd Univ Petr & Minerals Dept Elect Engn Dhahran 31261 Saudi Arabia

ISBN: (纸本)0819437646

An advanced seismic compression technique is proposed to manage seismic data in a world of ever increasing data volumes in order to maintain productivity without compromising interpretation results. A separable 3-D discrete wavelet transform (DWT) using long biorthogonal filters is used. The computation efficiency of the DWT is improved by factoring the wavelet filters using the lifting scheme. In addition, the lifting scheme offers: 1) a dramatic reduction of the required auxiliary memory, 2) an efficient combination with parallel rendering algorithms to perform arbitrary surface and volume rendering for interactive visualization, and 3) an easy integration in the parallel I/O seismic data loading routines. The proposed technique is tested on a seismic volume from the Stratton field in South Texas. The resulting 3-level multiresolution decomposition yields 21 detail sub-volumes and a unique low-resolution sub-volume. The detail wavelet coefficients are quantized with an adaptive threshold uniform scalar quantizer (TUSQ). The scale-dependent thresholds are determined with the Stein unbiased risk estimate (SURE) principle. As the approximation coefficients represent a smooth low-resolution version of the input data they are only quantized using a uniform scalar quantizer (USQ). Finally a runlength plus a Huffman encoding are applied for binary coding of the quantized coefficients.

关键词： lifting scheme biorthogonal wavelets seismic volume 3-D compression wavelet shrinkage SureShrink

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wavelet integrated residual dictionary training for single image super-resolution

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MULTIMEDIA TOOLS AND applications 2019年第19期78卷 27683-27701页

作者： Deeba, Farah Ku, She Wang, Wenyong Ahmed, Junaid Qadir, Bahzad Univ Elect Sci & Technol China Sch Informat & Software Engn Chengdu Sichuan Peoples R China Univ Elect Sci & Technol China Sch Comp Sci & Engn Chengdu Sichuan Peoples R China Sukkur IBA Univ Dept Elect Engn Sukkur Pakistan Mehran Univ Engn & Technol Dept Telecommun Jamshoro Pakistan

Single image super-resolution (SISR) is an encouraging process for image processing applications, where the super-resolution (SR) problem is resolved by the sparse representation of a signal. This paper presents a novel single image SR algorithm based on sparse representation in the wavelet domain. Dictionary learning and sparse coding process for acquiring the high-frequency details into the wavelet domain, wavelet gives better results because wavelets promote the sparsity of the image and also provide structural information about the image. Our method combines the concept of residual image learning with the coupled K-SVD dictionary learning algorithm. Residual image is obtained by subtracting the level-one DWT approximation sub-band from level-two DWT approximation sub-band. The proposed method combined properties of wavelet decomposition such as preserving the high-frequency details, directionality-and compactness by learning the multiple dictionaries for each wavelet subband. Six wavelets integrated residual dictionaries are learned from level-one and level-two wavelet sub-band images, three for the low resolution and three for high-resolution wavelet subband images. By designing compact wavelet multiple dictionaries, we reduce the computational complexity as the number of dictionary atoms is small. The proposed algorithm is tested over four different datasets and compared with eight other super-resolution algorithms in terms of average Peak signal-to-noise ratio (PSNR) and Structural similarity index measure (SSIM). We also examine how super-resolved image can be affected by the dictionary size and patch size as these parameters play a very important role in reconstructing the HR images.

关键词： Discrete wavelet transform (DWT) Sparse representation (SR) Single image super-resolution (SISR) Dictionary learning

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RNS-FPL merged architectures for orthogonal DWT

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ELECTRONICS LETTERS 2000年第14期36卷 1198-1199页

作者： Ramírez, J García, A Fernández, PG Parrilla, L Lloris, A Dept Elect & Tecnol Comp Granada 18071 Spain Escuela Politecn Super Dept Ingn Elect Jaen 23071 Spain

Novel. regular, compact and easily scalable residue number system (RNS) field-programmable logic (FPL) merged architectures for the orthogonal 1D discrete wavelet transform (DWT) and 1D inverse discrete wavelet transform (IDWT) are presented. These structures halve the number of look-up tables (LUTs) required per octave, providing a sustained throughput independent of the input data and filter coefficient precision. Thy are suitable to be considered as the core of 2D DWT processors for high data rate image processing applications.

关键词： look-up tables field-programmable logic merged architectures 2D DWT processors optical logic table lookup filter coefficient precision Optical computing techniques 1D inverse discrete wavelet transform Optical computers, logic elements, and interconnects high data rate image processing applications RNS-FPL merged architectures sustained throughput field programmable gate arrays image processing input data Computer vision and image processing techniques image processing and restoration orthogonal DWT Optical, image and video signal processing discrete wavelet transforms orthogonal 1D discrete wavelet transform easily scalable residue number system Optical logic devices and optical computing techniques Optical logic elements

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wavelet based robust sub-band features for phoneme recognition

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IEE PROCEEDINGS-VISION image AND signal processing 2004年第3期151卷 187-193页

作者： Farooq, O Datta, S Aligarh Muslim Univ Dept Elect Engn Aligarh 202002 Uttar Pradesh India Loughborough Univ Technol Dept Elect & Elect Engn Loughborough LE11 3TU Leics England

wavelet transform has been found to be an effective tool for the time-frequency analysis of non-stationary and quasi-stationary signals. Recent years have seen wavelet transform being used for feature extraction in speech recognition applications. In the paper a sub-band feature extraction technique based on an admissible wavelet transform is proposed and the features are modified to make them robust to additive white Gaussian noise. The performance of this system is compared with the conventional mel frequency cepstral coefficients (MFCC) under various signal to noise ratios. The recognition performance based on the eight sub-band features is found to be superior under the noisy conditions compared with MFCC features.

关键词： .wavelet transform STFT white Gaussian sub-band based features phoneme recognition

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Performance comparison of masking models based on a new psychovisual test method with natural scenery stimuli

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signal processing-image COMMUNICATION 2002年第10期17卷 807-823页

作者： Nadenau, MJ Reichel, J Kunt, M VisioWave Corp CH-1024 Ecublens Switzerland Swiss Fed Inst Technol Signal Proc Lab CH-1015 Lausanne Switzerland

Various image processing applications exploit a model of the human visual system (HVS). One element of HVSmodels describes the masking-effect, which is typically parameterized by psycho-visual experiments that employ superimposed sinusoidal stimuli. Those stimuli are oversimplified with respect to real images and can capture only very elementary masking-effects. To overcome these limitations a new psycho-visual test method is proposed. It is based on natural scenery stimuli and operates in the wavelet domain. The collected psycho-visual data is finally used to evaluate the performance of various masking models under conditions as found in real image processing applications like compression. (C) 2002 Elsevier Science B.V. All rights reserved.

关键词： HVS masking wavelet image compression quality assessment

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Demystifying the applications of wavelet Transform in Bio signal and Medical Data processing 1

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8th Smart Trends in Computing and Communications (SmartCom)

作者： Antony, Linu Tess Kumar, S. N. APJAKTU Amal Jyothi Coll Engn Trivandrum Kerala India

ISBN: (数字)9789819713202

ISBN: (纸本)9789819713196;9789819713202

wavelet transforms have revolutionized bio signal and medical image processing by providing powerful tools for analysis and feature extraction. This article aims to demystify the applications of wavelet transforms in these domains, focusing on the DWT and WPT. These transforms allow for localized representation of signals and images in both time and frequency domains, enabling detailed analysis and relevant information extraction. In bio signal processing, wavelet transforms are used for denoising, feature extraction, and classification of ECG, EEG, and other bio signals. In medical image processing, wavelet transforms find applications in denoising, compression, image fusion, and segmentation, leading to improved analysis and diagnosis. The article provides an overview of these applications, highlighting advantages and presenting specific examples and results. wavelet transforms have great potential in bio signal and medical image processing, driving healthcare and biomedical research advancements.

关键词： wavelet transforms image processing signal processing Quaternion wavelet transforms Gabor wavelet transforms

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image Noise Reduction Based on a Fixed wavelet Frame and CNNs Applied to CT

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IEEE TRANSACTIONS ON image processing 2021年 30卷 9386-9401页

作者： Zavala-Mondragon, Luis Albert de With, Peter H. N. van der Sommen, Fons Eindhoven Univ Technol Dept Elect Engn VCA Lab NL-5612 Eindhoven Netherlands

Radiation exposure in CT imaging leads to increased patient risk. This motivates the pursuit of reduced-dose scanning protocols, in which noise reduction processing is indispensable to warrant clinically acceptable image quality. Convolutional Neural Networks (CNNs) have received significant attention as an alternative for conventional noise reduction and are able to achieve state-of-the art results. However, the internal signal processing in such networks is often unknown, leading to sub-optimal network architectures. The need for better signal preservation and more transparency motivates the use of wavelet Shrinkage Networks (WSNs), in which the Encoding-Decoding (ED) path is the fixed wavelet frame known as Overcomplete Haar wavelet Transform (OHWT) and the noise reduction stage is data-driven. In this work, we considerably extend the WSN framework by focusing on three main improvements. First, we simplify the computation of the OHWT that can be easily reproduced. Second, we update the architecture of the shrinkage stage by further incorporating knowledge of conventional wavelet shrinkage methods. Finally, we extensively test its performance and generalization, by comparing it with the RED and FBPConvNet CNNs. Our results show that the proposed architecture achieves similar performance to the reference in terms of MSSIM (0.667, 0.662 and 0.657 for DHSN2, FBPConvNet and RED, respectively) and achieves excellent quality when visualizing patches of clinically important structures. Furthermore, we demonstrate the enhanced generalization and further advantages of the signal flow, by showing two additional potential applications, in which the new DHSN2 is used as regularizer: (1) iterative reconstruction and (2) ground-truth free training of the proposed noise reduction architecture. The presented results prove that the tight integration of signal processing and deep learning leads to simpler models with improved generalization.

关键词： Noise reduction Discrete wavelet transforms Wireless sensor networks Computed tomography Kernel Convolution image reconstruction CNN wavelets shrinkage encoder-decoder denoising

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