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Block motion estimation using wavelet filtering

Block motion estimation using wavelet filtering

引用

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

作者： H.F. Ates M.T. Orchard Electrical Engineering Department Princeton University Princeton NY USA

Block matching motion compensation achieves savings in residual error energy at the cost of motion vector bit rate. While this tradeoff has proven valuable for many video sequences, there are many obvious examples of blocks for which the cost of motion vectors is not offset by the gains of motion compensation. This paper proposes a multiresolution framework for motion compensated prediction that offers a richer set of options for trading off motion compensation accuracy against the cost of motion vectors. The method improves the prediction at motion boundaries and on covered/uncovered regions, while reducing the bit rate by using less accurate motion vectors in smoother regions. The new algorithm is compared to the full-search block matching in various simulations, and the results show that the algorithm achieves a 10 to 30% reduction in motion vector bit rate. These saving are particularly important in low bit rate applications where motion overhead constitutes a significant percentage of overall bit rate.

关键词： Motion estimation Bit rate Costs Frequency Entropy Motion compensation Predictive models Filtering image sequences image coding

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Face recognition using Homomorphic Filtering as a pre-processing technique

Face recognition using Homomorphic Filtering as a pre-proces...

引用

International conference on Emerging Trends in Communication, Control, signal processing & Computing applications (C2SPCA)

作者： Nihal Winston D'Cunha Sachin A Birajdhar K Manikantan S Ramachandran Dept. of Electronics and Communication Engg. M.S. Ramaiah Inst. of Tech. Bangalore INDIA Dept. of Electronics and Communication Engg. S.J.B. Inst. of Tech. Bangalore INDIA

The appearance of the face will vary drastically when illumination, pose and expression change. Variations in these conditions make Face Recognition (FR) an even more challenging and difficult task. In this paper, we propose three novel techniques, viz., Homomorphic Filtering (HF), image Flip (IF) and Raster Scan Mapping (RSM), to improve the performance of a FR system. HF is a pre-processing technique used to normalize the illumination variations. IF is used to neutralize pose and expression variations. RSM is used to map 2D to 1D, to increase the ability to store the intensity value of all the spotted lines. DWT and DCT are used for efficient feature extraction and a Binary Particle Swarm Optimization based feature selection algorithm is used to search the feature space for the optimal feature subset. Experimental results show the promising performance of the proposed techniques for FR on four benchmark face databases, namely, CMUPIE, Color FERET, UMIST and ORL databases.

关键词： Databases Lighting Feature extraction Discrete wavelet transforms Hafnium Discrete cosine transforms Vectors

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Design and implementation of a novel architecture for symmetric FIR filters with boundary handling on Xilinx Virtex FPGAs

Design and implementation of a novel architecture for symmet...

引用

IEEE International conference on Field-Programmable Technology (FPT)

作者： A. Benkrid K. Benkrid D. Crookes School of Computer Science Queen''s University Belfast Belfast UK

Symmetric FIR filters, which provide linear phases, are frequently used in digital signal processing. This paper presents the design and implementation of a novel architecture for symmetric FIR filters on Xilinx Virtex FPGAs. The architecture is particularly useful for handling the problem of processing signal boundaries, which occurs in finite length signal processing (e.g. image processing). Based on bit parallel arithmetic, our architecture is fully scalable and parameterised. It takes into account the details of the symmetry and exploits the features of Xilinx Virtex FPGAs. The implementation leads to considerable area savings compared to conventional implementations (based on a hard router), at the expense of using a clock doubler, which reduces the overall processing speed. The latter is however still high enough to achieve real time performance. Moreover, our architecture can match the speed of a conventional implementation if the filter output is going to be decimated, as it is the case in multirate applications (e.g. wavelets).

关键词： Finite impulse response filter Field programmable gate arrays Nonlinear filters signal processing Clocks Convolution Computer architecture Discrete wavelet transforms Reflection Hardware

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Shearlet transform based image denoising using histogram thresholding

Shearlet transform based image denoising using histogram thr...

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International conference on Communication Systems and Networks (ComNet)

作者： T S Anju N R Nelwin Raj Department of ECE Sree Chitra Thirunal College of Engineering Trivandrum Kerala

ISBN: (纸本)9781509033508

This paper presents an efficient image denoising method by incorporating shearlet-based histogram thresholding. Nowadays, digital images are used in wide range of applications but most of these images are degraded during transmission and acquisition process. Removal of noise from images is still a challenging task for many researchers because there is always a trade-off between noise removal and fine edge preservation. This paper is based on image denoising using shearlet transform. Shearlets have excellent features for data analysis and processing, which overcomes the limitation of traditional methods. They are optimally sparse and have multi-scale and multi-directional properties which are optimal in representing image containing edges. In this paper, the proposed method is found to produce superior peak signal-to-noise ratio (PSNR) over the conventional denoising algorithms.

关键词： Histograms image denoising Noise reduction Algorithm design and analysis image edge detection wavelet transforms

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Optimizing Compressed Sensing with Taylor Sunflower Algorithm: A Performance Analysis of Hybrid Techniques for Biomedical Imaging and Video Surveillance

Optimizing Compressed Sensing with Taylor Sunflower Algorith...

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

作者： Asha Rajiv Santosh Kumar S. Arul Antran Vijay Prabhat Sharma Ranjusha J P Prateek Aggarwal School of Sciences Physics & Electronics JAIN (Deemed to be University) Bangalore Karnataka India AIDS Vishwakarma Institute of Technology Pune India Department of Computer Science Engineering Karpagam College of Engineering Coimbatore India Centre of Research Impact and Outcome Chitkara University Rajpura Punjab India Department of S&H Prince Shri Venkateshwara Padmavathy Engineering College Chennai India Chitkara Centre for Research and Development Chitkara University Himachal Pradesh India

ISBN: (数字)9798350368949

ISBN: (纸本)9798350368956

A powerful area that seeks to minimize the amount of space of signals that are compressible or sparse in a specific base representation that uses compressed recognizing in video streaming applications. A sparse signal provides a good approximation of the signal since the representation is sparse and the amplitude of the coefficient’s decays according to the power law. The process of projecting signals into a space with low dimensions results in measurement vectors. A sparse signal may be accurately reconstructed using the measurement vector. Imprecise or noisy data that may nevertheless be compressed and used for approximation purposes. images in the frequency or wavelet domain are suitable for Compressed Sensing because they are inherently compressible. Reconstructed images are of higher quality, and the compression ratio—the ratio of an image's pixel count to its measurement vector—has been reduced, enabling more precise reconstruction. Taylor SFO based CS is a two-stage optimization technique for picture compression and recovery that aims to enhance the quality of models with high correlation and low redundancy. An image that uses self-replication and 3D change to compress itself is first processed in the early phase. In the last step, the CS uses SSIM to recover pictures using an ensemble sparseness estimate and Taylor SFO parameters.

关键词： image coding wavelet domain Three-dimensional displays Frequency-domain analysis Biomedical measurement Streaming media Extraterrestrial measurements Vectors Noise measurement image reconstruction

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Fractal image processing and Analysis for Compression of Hyperspectral images

Fractal Image Processing and Analysis for Compression of Hyp...

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International conference on Computing and Networking Technology (ICCNT)

作者： Tripty Singh Tina Babu Amrita School of Engineering Amrita Vishwa Vidyapeetham Bengaluru India

Hyperspectral imaging is a technique in which the information is collecting and processing across an electromagnetic spectrum. Fractal image processing deals with various dimensions of an image. Hyperspectral analysis are applied on multiple frequency bands of images, and produces spatial and spectral information. HSI can elongate 390 to 700 nm i.e UV to infrared and near-infrared wavelength regions. This technique based system engenders narrow band() images of different wavelengths. By utilizing HIS, visualization can be elongated to invisible wavelengths. Hyperspectral based techniques have applications in various fields such as medical diagnosis, agriculture, food processing, remote sensing etc. In this project, the compression of hyperspectral images is considered. Present work involves Modified DCT-Discrete Cosine Transformation based image compression. Compared with the convention lossless compression techniques of the benchmark multi-component and hyperspectral (JPEG2000), the MOD-DCT lossless algorithm produces considerable reduce in compressed file size for beyond visible range. After implementation of Modified DCT for hyperspectral images high compression ratio was achieved.

关键词： image coding Hyperspectral imaging Discrete cosine transforms Quantization (signal) Fractals wavelet domain

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An Efficient Algorithm for Compressed MR Imaging using Total Variation and wavelets

An Efficient Algorithm for Compressed MR Imaging using Total...

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26th IEEE conference on Computer Vision and Pattern Recognition (CVPR 2008), vol.2

作者： Shiqian Ma Wotao Yin Yin Zhang Amit Chakraborty Department of IEOR Columbia University New York NY USA Department of CAAM Rice University Houston TX USA Integrated Data Systems Siemens AG Corporate Research and Development Princeton NJ USA

Compressed sensing, an emerging multidisciplinary field involving mathematics, probability, optimization, and signal processing, focuses on reconstructing an unknown signal from a very limited number of samples. Because information such as boundaries of organs is very sparse in most MR images, compressed sensing makes it possible to reconstruct the same MR image from a very limited set of measurements significantly reducing the MRI scan duration. In order to do that however, one has to solve the difficult problem of minimizing nonsmooth functions on large data sets. To handle this, we propose an efficient algorithm that jointly minimizes the l{sub}1 norm, total variation, and a least squares measure, one of the most powerful models for compressive MR imaging. Our algorithm is based upon an iterative operator-splitting framework. The calculations are accelerated by continuation and takes advantage of fast wavelet and Fourier transforms enabling our code to process MR images from actual real life applications. We show that faithful MR images can be reconstructed from a subset that represents a mere 20 percent of the complete set of measurements.

关键词： signal processing algorithms image reconstruction image coding Compressed sensing Iterative algorithms Mathematics signal processing Magnetic resonance imaging Least squares methods Acceleration

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Resolution mosaic-based Smart Camera for video surveillance

Resolution mosaic-based Smart Camera for video surveillance

引用

ACM/IEEE International conference on Distributed Smart Cameras, ICDSC

作者： Mohammed A-Megeed Salem Kristian Klaus Frank Winkler Beate Meffert Faculty of Computer and Information Sciences Ain Shams University Cairo Egypt Signal Processing and Pattern Recognition Group Department of Computer Science Humboldt-Universitaet zu Berlin Berlin Germany

Video surveillance is one of the most data intensive applications. A typical video surveillance system consists of one or multiple video cameras, a central storage unit, and a central processing unit. At least two bottlenecks exist: First, the transmission capacity is limited, especially for raw data. Second, the central processing unit has to process the incoming data to give results in real time. Therefore, we propose an FPGA-based embedded camera system which performs all steps of image acquisition, region of interest extraction, generation of a multiresolution image, and image transmission. The proposed pipeline-based architecture allows a real time wavelet-based image segmentation and a detection of moving objects for surveillance purposes. The system is integrated in a single FPGA using external RAM as storage for images and for a Linux operating system which controls the data flow. With the pipeline concept and a Linux device driver it is possible to create a system for streaming the results of an image processing through a GbE interface. A real time processing is achieved. The camera transmits the captured images with 30 Mpixel/s , but the system is able to process 100 Mpixel/s .

关键词： Smart cameras Video surveillance Central processing Unit image storage Linux Data mining image resolution image communication image segmentation Object detection

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Implementation of noise reduction for PET using hybrid nonlinear wavelet shrinkage method

Implementation of noise reduction for PET using hybrid nonli...

引用

IEEE Symposium on Nuclear Science (NSS/MIC)

作者： Akihiro Ishikawa Keishi Kitamura Tetsuro Mizuta Kazumi Tanaka Masaharu Amano Yoshihiro Inoue Medical Systems Division Shimadzu Corporation Kyoto Japan Shimadzu Corporation Japan

Positron emission tomography (PET) imaging provides the functional information and precise physiological uptake of radioactivity in a patient's body. But the photon level of PET system has fewer level, the shortcoming of PET is low SNR. The quantum noise in emission data can have a significant influence on the statistical uncertainty of PET measurements. The reduction of the quantum noise in images is an important issue. Conventional post-processing is not completely suitable, since it leads to loss of the spatial resolution while reducing noise. In this study, we present a method for denoising the quantum noise in images. Our proposed methods is to use a hybrid wavelet shrinkage processing in 3D sinogram(bin,angle,slice) domain. One is applied to axial(bin,angle) space to reduce detector dependence noise element, the other is applied to 3D space to reduce quantum noise. We optimized three parameters in each wavelet processing. The efficacy of the filtering technique was shown on a derenzo phantom. Finally, we also performed human FDG studies to evaluate clinical performance. As result, we have found that the optimized parameters and reconstructed image without post filtering in proposed study reduce COV while maintaining average and spatial resolution. We therefore conclude that wavelet de-noising technique is an effective tool to process PET images and thus to provide better image quality. We expect that our study can provide benefits to clinical applications of PET images.

关键词： Noise reduction Positron emission tomography Spatial resolution Filtering signal to noise ratio Measurement uncertainty Noise measurement wavelet domain Detectors Imaging phantoms

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image watermarking using dual-tree complex wavelet by coefficients swapping and group of coefficients quantization

Image watermarking using dual-tree complex wavelet by coeffi...

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IEEE International conference on Multimedia and Expo (ICME)

作者： Huijuan Yang Xudong Jiang Alex C. Kot Nanyang Technological University Singapore

In this paper, we present two watermarking schemes for images using the low-pass frequency coefficients and high-pass complex frequency coefficients of the Dual-Tree Complex wavelet Transform (DT-CWT) of the image, the distinctive characteristics of the two schemes can lead to different applications. We investigate the problem of embedding binary watermark sequence in DT-CWT domain, which is a challenging problem. The coefficients swapping is done by carefully selecting those 2×2 blocks with high-energy in the low frequency and swap the coefficients that lie in the median range in the block to embed the watermark. Whereas the group of coefficients quantization quantizes a group of high-pass complex frequency coefficients to make the quantized coefficients lie in the middle of the quantization range and distribute the changes among the coefficients. Experimental results conducted on 100 standard images achieve about 96% and 92% detection rate for the low-pass and high-pass frequency coefficients-based schemes, respectively. Robustness tests against some common signal processing attacks such as additive noise, median filtering and lossy JPEG compression confirm the superior performance of the proposed schemes.

关键词： Watermarking Robustness Quantization Visualization PSNR Transforms image edge detection

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