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检索条件"任意字段=International Workshop on Local and Non-Local Approximation in Image Processing, LNLA"

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nonlocal image DEBLURRING: VARIATIONAL FORMULATION WITH nonlocal COLLABORATIVE L₀-NORM PRIOR

NONLOCAL IMAGE DEBLURRING: VARIATIONAL FORMULATION WITH NONL...

引用

international workshop on local and non-local approximation in image processing

作者： Katkovnik, Vladimir Egiazarian, Karen Tampere Univ Technol Dept Signal Proc FIN-33101 Tampere Finland

ISBN: (纸本)9781424435142

Spatially adaptive nonlocal patch-wise estimation is one of the most promising recent directions in image processing. Within this framework a set of the state-of-the-art Block Matching 3-D (BM3D) algorithms has been developed for different imaging problems [1]-[5]. Recently, a special prior has been proposed allowing to reformulate the multi-stage hard-thresholding BM3D denoising as global minimization of an energy criterion [6]. The outstanding performance of BM3D works as a strong argument in favor of this prior giving an efficient multilayer redundant image model. The variational formulation is used in [6] in order to design a novel recursive denoising algorithm. In this paper the nonlocal collaborative l(0)-norm prior is a tool to design deblurring algorithms, where the global penalty function works as an adaptive regularizator. The main contribution concerns the development and testing of algebraic and frequency domain recursive algorithms minimizing the global criterion. Simulation demonstrate a very good performance of the novel algorithms.

关键词： Frequency domain analysis

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LOSSY COMPRESSION OF imageS CORRUPTED BY MIXED POISSON AND ADDITIVE GAUSSIAN NOISE

LOSSY COMPRESSION OF IMAGES CORRUPTED BY MIXED POISSON AND A...

引用

international workshop on local and non-local approximation in image processing

作者： Lukin, V. V. Krivenko, S. S. Zriakhov, M. S. Ponomarenko, N. N. Abramov, S. K. Kaarna, A. Egiazarian, K. Natl Aerosp Univ Dept Transmitters Receivers & Signal Proc 17 Chkalova St UA-61070 Kharkov Ukraine Lappeenranta Univ Technol Dept Informat Technol FIN-53851 Lappeenranta Finland Tampere Univ Technol Inst Signal Proc FIN-33101 Tampere Finland

ISBN: (纸本)9781424435142

Lossy compression of images corrupted by mixed Poisson and additive Gaussian noise is considered. Peculiarities of noise filtering effects observed for two approaches to image compression are studied. This is first done for specially created artificial image and, then, for two standard test images. It is shown that the standard (direct) approach to lossy compression leads to different degrees of noise suppression depending upon image mean in homogeneous regions. The use of root transformation leads to better results. Moreover, coder parameters can be adjusted to mixed noise parameters if they are known a priori. Then it becomes possible to reach optimal operation point (OOP), i.e., such compression ratio (CR) or bpp that provide maximal peak signal-to noise ratio (PSNR) or minimal MSE calculated between compressed and noise free images without having an actual noise-free image. A provided CR can be about 10...30 depending upon image complexity.

关键词： image compression

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SIFT-Based local image Description Using Sparse Representations

SIFT-Based Local Image Description Using Sparse Representati...

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IEEE international workshop on Multimedia Signal processing

作者： Zepeda, Joaquin Kijak, Ewa Guillemot, Christine Ctr Rennes Bretagne Atlantique INRIA Rennes France Univ Rennes 1 Ctr Rennes Bretagne Atlantique INRIA IRISA Rennes France

ISBN: (纸本)9781424444632

This paper addresses;the problem of efficient SIFT-based image description and searches in large databases within the framework of local querying. A descriptor called (lie bag-of-features has been introduced in [I] which first vector quantizes SIFT descriptors and then aggregates (lie set of resulting codeword indices (so-called visual words) into a histogram of occurence of the different visual words in the image. The aim is to make the image search complexity tractable by transforming the set of local image descriptor vectors into a single sparse vector as sparsity particularly permits efficient inner product calculations. However. aggregating local descriptors into a single histogram decreases the discerning power of the System when performing local queries. In this paper, we propose a new approach that aims to enjoy the complexity benefits of Sparsity while at the same time retaining the local quality of the input descriptor vectors. This is accomplished by searching for a sparse approximation of the input SIFT descriptors. The sparse approximation yields a sparse vector per local SIFT descriptor, and helps preserving local description properties by using each sparse-transformed descriptor independently in a voting system to retrieve indexed images. Our system is shown experimentally to perform better than histogram based systems tinder query locality, albeit at an increased complexity.

关键词： Vectors

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Design of orthogonal transforms based on partition of unity method and their application to image compression

Design of orthogonal transforms based on partition of unity ...

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international workshop on local and non-local approximation in image processing, lnla

作者： Samvel Khachatryan Lilit Minasyan State Engineering University of Armenia Armenia

In this work a new complete system of trigonometric functions for representation of finite continuous functions is defined. New discrete orthogonal transforms are synthesized by discretization and orthonormalization of the proposed system of functions. An image compression algorithm using the synthesized transform is developed. The proposed algorithm illustrates high compression ratio and good reconstruction quality as confirmed by a numerous of experiments.

关键词： image coding Discrete transforms Discrete wavelet transforms Discrete cosine transforms Partitioning algorithms Continuous wavelet transforms Digital images Design engineering image reconstruction Focusing

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High capacity reversible data hiding based on histogram shifting and non-local means

High capacity reversible data hiding based on histogram shif...

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international workshop on local and non-local approximation in image processing, lnla

作者： V. Conotter G. Boato M. Carli K. Egiazarian Department of Information Engineering and Computer Science University of Trento Trento Italy Applied Electronics Department University of Roma TRE Rome Italy Institute of Signal Processing Tampere University of Technology Tampere Finland

In this paper we propose a new reversible data hiding framework which applies prediction in the embedding procedure by suitably modifying the prediction errors and exploits non-local similarity in the prediction phase to estimate the to-be-predicted value. This results in a scheme which can be jointly used with different predictors and allows reaching high embedding capacity while preserving a high image quality. Extensive simulations demonstrate the effectiveness of the proposed approach.

关键词： Data encapsulation Histograms

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Cross-color BM3D filtering of noisy raw data

Cross-color BM3D filtering of noisy raw data

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international workshop on local and non-local approximation in image processing, lnla

作者： Aram Danielyan Markku Vehvilainen Alessandro Foi Vladimir Katkovnik Karen Egiazarian Department of Signal Processing Tampere University of Technology Tampere Finland Nokia Research Center Tampere Finland

Color image reconstruction from noisy color filter array (CFA) data is considered. A modification of the Block Matching 3D (BM3D) filter for CFA data denoising utilizing cross-color correlations is proposed. Denoised images are then demosaicked by algorithms developed for noise-free data leading to state-of-the-art performance for both Gaussian and Poissonian noise models.

关键词： Filtering Noise reduction Interpolation Colored noise Gaussian noise image reconstruction Principal component analysis Additive white noise Electronic mail Signal processing algorithms

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Quality assessment measure based on image structural properties

Quality assessment measure based on image structural propert...

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international workshop on local and non-local approximation in image processing, lnla

作者： David Asatryan Karen Egiazarian Institute of Informatics and Automation Problems of NAS ARMENIA Yerevan Armenia Department of Signal Processing Tampere University of Technology Tampere Finland

In this paper, a new objective quality assessment measure for images is proposed based on statistical structural image analysis using Weibull model and Cramer-von Mises statistics. It is estimated via proximity of parameters of empirical distributions of a gradient magnitude of pixel intensities. Results of numerical experiments demonstrate that the proposed measure is more adequate to perception by human visual system than the usual pixel-by-pixel measures. Unlike other quality assessment measures, a new one can be used on not well-aligned images or on images having different sizes.

关键词： Quality assessment Cameras Fourier transforms image sensors Optical sensors image sequences Mobile robots Robot sensing systems Surveillance Signal analysis

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Normalized Cross-Correlation using SOFT

Normalized Cross-Correlation using SOFT

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international workshop on local and non-local approximation in image processing, lnla

作者： Benjamin Huhle Timo Schairer Wolfgang Strasser WSI/GRI University of Tübingen Tubingen Germany

We present a novel method to compute the normalized cross-correlation (NCC) of spherical signals such as omnidirectional images for the purpose of image alignment, i.e. orientation estimation, or template tracking. Expanding the reference image and the search template in the spherical harmonics basis, we use the Fourier representation of SO(3) as introduced by Kostelec and Rockmore. We show that the normalized version of the cross-correlation function with regard to orientation can be expressed in terms of standard correlation terms and the NCC can therefore be computed efficiently in O (N 3 log 2 (N)). The approach is validated with experiments on real image sequences in a template matching application.

关键词： Cameras Fourier transforms image sensors Optical sensors image sequences Mobile robots Robot sensing systems Surveillance Signal analysis Algorithm design and analysis

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On the inversion of the Anscombe transformation in low-count Poisson image denoising

On the inversion of the Anscombe transformation in low-count...

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international workshop on local and non-local approximation in image processing, lnla

作者： Markku Makitalo Alessandro Foi Department of Signal Processing Tampere University of Technology Tampere Finland

The removal of Poisson noise is often performed through the following three-step procedure. First, the noise variance is stabilized by applying the Anscombe root transformation to the data, producing a signal in which the noise can be treated as additive Gaussian noise with unitary variance. Second, the noise is removed using a conventional denoising algorithm for additive white Gaussian noise. Third, an inverse transformation is applied to the denoised signal, obtaining the estimate of the signal of interest. The choice of the proper inverse transformation is crucial in order to minimize the bias error which arises when the nonlinear forward transformation is applied. We present an experimental analysis using a few state-of-the-art denoising algorithms and show that the estimation can be consistently improved by applying the exact unbiased inverse, particularly at the low-count regime.

关键词： image denoising Delta modulation

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Efficient design of a low redundant Discrete Shearlet Transform

Efficient design of a low redundant Discrete Shearlet Transf...

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international workshop on local and non-local approximation in image processing, lnla

作者： Bart Goossens Jan Aelterman Hiep Luong Aleksandra Pizurica Wilfried Philips Department of Telecommunications and Information Processing (TELIN-IPI-IBBT) Ghent University Ghent Belgium

There has been a huge interest in multiresolution representations that also perform a multidirectional analysis. The Shearlet transform provides both a multiresolution analysis (such as the wavelet transform), and at the same time an optimally sparse image-independent representation for images containing edges. Existing discrete implementations of the Shearlet transform have mainly focused on specific applications, such as edge detection or denoising, and were not designed with a low redundancy in mind (the redundancy factor is typically larger than the number of orientation subbands in the finest scale). In this paper, we present a novel design of a discrete Shearlet transform, that can have a redundancy factor of 2.6, independent of the number of orientation subbands, and that has many interesting properties, such as shift-invariance and self-invertability. This transform can be used in a wide range of applications. Experiments are provided to show the improved characteristics of the transform.

关键词： Discrete transforms Discrete wavelet transforms image edge detection Performance analysis Multiresolution analysis Wavelet analysis Information processing Frequency image resolution image analysis

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