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检索条件"任意字段=Image Processing: Algorithms and Systems VI"

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image restoration by sparse 3D transform-domain collaborative filtering

Image restoration by sparse 3D transform-domain collaborativ...

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Conference on image processing - algorithms and systems vi

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

ISBN: (纸本)9780819469847

We propose an image restoration technique exploiting regularized inversion and the recent block-matching and 3D filtering (BM3D) denoising filter. The BM3D employs a non-local modeling of images by collecting similar image patches in 3D arrays. The so-called collaborative filtering applied on such a 3D array is realized by transform-domain shrinkage. In this work, we propose an extension of the BM3D filter for colored noise, which we use in a two-step deblurring algorithm to improve the regulaxization after inversion in discrete Fourier domain. The first step of the algorithm is a regularized inversion using BM3D with collaborative hard-thresholding and the seconds step is a regularized Wiener inversion using BM3D with collaborative Wiener filtering. The experimental results show that the proposed technique is competitive with and in most cases outperforms the current best image restoration methods in terms of improvement in signal-to-noise ratio.

关键词： image restoration deconvolution deblurring block-matching collaborative filtering

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Edge preserving image enhancement using anisotropic diffusion

Edge preserving image enhancement using anisotropic diffusio...

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Conference on image processing - algorithms and systems vi

作者： Wharton, Eric J. Panetta, Karen A. Agaian, Sos S. Tufts Univ Dept Elect & Comp Engn 161 Coll Ave Medford MA 02155 USA Univ Texas San Antonio Coll Engn San Antonio TX 78249 USA

ISBN: (纸本)9780819469847

Enhancing an image in such a way that maintains image edges is a difficult problem. Many current methods for image enhancement either smooth edges on a small scale while improving contrast on a global scale or enhance edges on a large scale while amplifying noise on a small scale. One method which has been proposed for overcoming this is anisotropic diffusion, which views each image pixel as an energy sync which interacts with the surrounding pixels based upon the differences in pixel intensities and conductance values calculated from local edge estimates. In this paper, we propose a novel image enhancement method which makes use of these smoothed images produced by diffusion methods. The basic steps of this algorithm are: a) decompose an image into a smoothed image and a difference image, for example by using anisotropic diffusion or as in Lee's Algorithm [14];b) apply two image enhancement algorithms, such as alpha rooting [7] or logarithmic transform shifting [15];c) fuse these images together, for example by weighting the two enhanced images and summing them for the final image. Computer simulations comparing the results of the proposed method and current state-of-the-art enhancement methods will be presented. These simulations show the higher performance, both on the basis of subjective evaluation and objective measures, of the proposed method over current methods.

关键词： anisotropic diffusion image enhancement adaptive filtering edge detection edge enhancement

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Bayesian anisotropic denoising in the Laguerre Gauss domain

Bayesian anisotropic denoising in the Laguerre Gauss domain

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Conference on image processing - algorithms and systems vi

作者： Ercole, Chiara Campisi, Patrizio Neri, Alessandro Univ Roma Roma Tre Dip Elettron Applicata I-00146 Rome Italy

ISBN: (纸本)9780819469847

In this contribution, we propose an adaptive multiresolution denoising technique operating in the wavelet domain that selectively enhances object contours, extending a restoration scheme based on edge oriented wavelet representation by means of adaptive surround inhibition inspired by the human visual system characteristics. The use of the complex edge oriented wavelet representation is motivated by the fact that it is tuned to the most relevant visual image features. In this domain, an edge is represented by a complex number whose magnitude is proportional to its "strength" while phase equals the orientation angle. The complex edge wavelet is the first order dyadic Laguerre Gauss Circular Harmonic Wavelet, acting as a band limited gradient operator. The anisotropic sharpening function enhances or attenuates large/small edges more or less deeply, accounting for masking effects induced by textured background. Adapting sharpening to the local image content is realized by identifying the local statistics of natural and artificial textures like grass, foliage, water, composing the background. In the paper, the whole mathematical model is derived and its performances are validated on the basis of simulations on a wide data set.

关键词： image denoising contour detection Bayesian estimation circular harmonic wavelet thresholding

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Classification based polynomial image interpolation

Classification based polynomial image interpolation

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Conference on image processing - algorithms and systems vi

作者： Lenke, Sebastian Schroeder, Hartmut Univ Dortmund D-44227 Dortmund Germany

ISBN: (纸本)9780819469847

Due to the fast migration of high resolution displays for home and office environments there is a strong demand for high quality picture scaling. This is caused on the one hand by large picture sizes and on the other hand due to an enhanced visibility of picture artifacts on these displays [1]. There are many proposals for an enhanced spatial interpolation adaptively matched to picture contents like e.g. edges. The drawback of these approaches is the normally integer and often limited interpolation factor. In order to achieve rational factors there exist combinations of adaptive and non adaptive linear filters, but due to the non adaptive step the overall quality is notably limited. We present in this paper a content adaptive polyphase interpolation method which uses "offline" trained filter coefficients and an "online" linear filtering depending on a simple classification of the input situation. Furthermore we present a new approach to a content adaptive interpolation polynomial, which allows arbitrary polyphase interpolation factors at runtime and further improves the overall interpolation quality. The main goal of our new approach is to optimize interpolation quality by adapting higher order polynomials directly to the image content. In addition we derive filter constraints for enhanced picture quality. Furthermore we extend the classification based filtering to the temporal dimension in order to use it for an intermediate image interpolation.

关键词： polynomial image interpolation adaptive filtering classification intermediate image interpolation

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Automated analysis of label-free spinning-disc microarray images

Automated analysis of label-free spinning-disc microarray im...

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Conference on image processing - algorithms and systems vi

作者： Krishnamurthi, Ganapathy Norwood, Tim Varma, Manoj Quadraspec Inc W Lafayette IN 47906 USA

ISBN: (纸本)9780819469847

We describe a set of methods to enable fully automated analysis of a novel label-free spinning-disc format microarray system. This microarray system operates in a dual-channel mode, simultaneously acquiring fluorescence as well as interferometric signals. The label-free interferometric component enables the design of robust gridding methods, which account for rotational effects difficult to estimate in traditional microarray image analysis. Printing of microarray features in a Cartesian grid is preferable for commercial systems because of the benefits of using existing DNA/protein printing technologies. The spinning disc operation of the microarray requires spatial transformation of Cartesian microarray features, from the reader/scanner frame of reference to the disc frame of reference. We describe a fast spatial transformation method with no measurable degradation in the quality of transformed data, for this purpose. The gridding method uses frequency-domain information to calculate grid spacing and grid rotation. An adaptive morphological segmentation method is used to segment microarray spots with variable sizes accurately. The entire process, from the generation of the raw data to the extraction of biologically relevant information, can be performed without any manual intervention, allowing for the deployment of high-throughput systems. These image analysis methods have enabled this microarray system to achieve superior sensitivity limits.

关键词： automated image analysis microarray label-free compact disc microarray BioCD

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Overview of DARPA MTO GPU program

Overview of DARPA MTO GPU program

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Conference on Independent Component Analyses, Wavelets, Unsupervised Nano-Biomimetic Sensors, and Neural Networks vi

作者： Healy, Dennis Braunreiter, Dennis Furtek, Jeremy Chen, Hai-Wen MTO DARPA (United States) Science Applications International Corp. (United States)

ISBN: (纸本)9780819471703

This paper reviews the DARPA MTO STAP-BOY program for both Phase I and II. The STAP-BOY program conducts fast covariance factorization and tuning techniques for space-time adaptive process (STAP) Algorithm Implementation on Graphics Processor unit (GPU) Architectures for Embedded systems. Emerging capabilities in stream and multi-core computation, along with high speed memory bandwidths in commercial GPU architectures, are enabling breakthrough low-cost and low-power teraflop computing solutions to DoD-embedded computing challenges. Under the DARPA MTO STAP-BOY program, SAIC and Duke University, in cooperation with commercial graphics processor companies, have been mapping complex signal processing algorithms to GPU architectures. algorithms undergoing implementation include STAP applications for radar adaptive beamforming and spin-image surface matching applications for object recognition in 3-D range-image data.

关键词： Signal processing Detection and tracking algorithms visualization Data modeling 3D image processing 3D modeling Computer architecture Fourier transforms Computer programming Object recognition

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Block artifact reduction in BMA-based super-resolution video processing

Block artifact reduction in BMA-based super-resolution video...

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Conference on image processing - algorithms and systems vi

作者： Sohn, Young Wook Kang, Moon Gi Yonsei Univ Dept Elect & Elect Engn Seodaemoon Ku Seoul 120749 South Korea

ISBN: (纸本)9780819469847

Super-resolution (SR) video processing method with reduced block artifacts using conventional block-matching algorithm (BMA) is proposed. To get high-quality SR results, accurate motion vectors are necessary in registration process. For real-time applications, block-based motion estimators are widely used, which show block-based motion errors if their motion vectors are employed for the registration. Incorrectly registered pixels due to the block-based motion errors limit the image quality improvement of the SR processing and even degrade the results by causing block artifacts. To reduce the artifacts from the inaccurately registered pixels, a weighting function using three-dimensional confidence measure is proposed in this paper. The measure uses spatial and inter-channel analysis to suppress the weight on incorrectly registered pixels during the SR process. Motion-compensated pixel differences and motion vector variances between previous and current frames are utilized for spatial analysis, and motion vector variance with constant acceleration model and pixel difference variances through LR frames are used for inter-channel analysis. Experimental results show significantly improved results in error regions keeping enhanced quality with accurately registered pixels, when motion vectors are found by conventional BMAs.

关键词： super-resolution block motion estimation motion reliability

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Measurement of annual ring width of log ends in forest machinery

Measurement of annual ring width of log ends in forest machi...

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Conference on image processing - algorithms and systems vi

作者： Marjanen, Kalle Ojala, Petteri Ihalainen, Heimo Tampere Univ Technol Inst Measurement & Informat Tampere 33720 Finland

ISBN: (纸本)9780819469847

The quality of wood is of increasing importance in wood industry. One important quality aspect is the average annual ring width and its standard deviation that is related to the wood strength and stiffness. We present a camera based measurement system for annual ring measurements. The camera system is designed for outdoor use in forest harvesters. Several challenges arise, such as the quality of cutting process, camera positioning and the light variations. In the freshly cut surface of log end the annual rings are somewhat unclear due to small splinters and saw marks. In the harvester the optical axis of camera cannot be set orthogonally to the log end causing non-constant resolution of the image. The amount of natural light in forest varies from total winter darkness to midsummer brightness. In our approach the image is first geometrically transformed to orthogonal geometry. The annual ring width is measured with two-dimensional power spectra. The two-dimensional power spectra combined with the transformation provide a robust method for estimating the mean and the standard deviation of annual ring width. With laser lighting the variability due to natural lighting can be minimized.

关键词： annual ring width outdoor imaging wood quality laser image based measurement power spectrum

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A fast non-local image denoising algorithm

A fast non-local image denoising algorithm

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Conference on image processing - algorithms and systems vi

作者： Dauwe, A. Goossens, B. Luong, H. Q. Philips, W. Univ Ghent IPI TELIN IBBT B-9000 Ghent Belgium

ISBN: (纸本)9780819469847

In this paper we propose several improvements to the original non-local means algorithm introduced by Buades et al. which obtains state-of-the-art denoising results. The strength of this algorithm is to exploit the repetitive character of the image in order to denoise the image unlike conventional denoising algorithms, which typically operate in a local neighbourhood. Due to the enormous amount of weight computations, the original algorithm has a high computational cost. An improvement of image quality towards the original algorithm is to ignore the contributions from dissimilar windows. Even though their weights are very small at first sight, the new estimated pixel value can be severely biased due to the many small contributions. This bad influence of dissimilar windows can be eliminated by setting their corresponding weights to zero. Using the preclassification based on the first three statistical moments, only contributions from similar neighborhoods are computed. To decide whether a window is similar or dissimilar, we will derive thresholds for images corrupted with additive white Gaussian noise. Our accelerated approach is further optimized by taking advantage of the symmetry in the weights, which roughly halves the computation time, and by using a lookup table to speed up the weight computations. Compared to the original algorithm, our proposed method produces images with increased PSNR and better visual performance in less computation time. Our proposed method even outperforms state-of-the-art wavelet denoising techniques in both visual quality and PSNR values for images containing a lot of repetitive structures such as textures: the denoised images are much sharper and contain less artifacts. The proposed optimizations can also be applied in other image processing tasks which employ the concept of repetitive structures such as intra-frame super-resolution or detection of digital image forgery.

关键词： non-local image denoising repetitive structures

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An algorithm for motion and change detection in image sequences based on chaos and information theory

An algorithm for motion and change detection in image sequen...

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image processing: algorithms and systems vi

作者： Farmer, M. Yuan, C. Department of Computer Science Engineering Science and Physics University of Michigan Flint 48502

ISBN: (纸本)9780819469847

Accurate and robust image change detection and motion segmentation has been of substantial interest in the image processing and computer vision communities. To date no single motion detection algorithm has been universally superior while biological vision systems are so adept at it. In this paper, we analyze image sequences using phase plots generated from sequential image frames and demonstrate that the changes in pixel amplitudes due to the motion of objects in an image sequence result in phase space behaviour resembling a chaotic signal. Recent research in neural signals have shown biological neural systems are highly responsive to chaos-like signals resulting from aperiodic forcing functions caused by external stimuli. We then hypothesize an alternative physics-based motion algorithm from the traditional optical flow algorithm. Rather than modeling the motion of objects in an image as a flow of grayscale values as in optical flow, we propose to model moving objects in an image scene as aperiodic forcing functions, impacting the imaging sensor, be it biological or silicon-based. We explore the applicability of some popular measures for detecting chaotic phenomena in the frame-wise phase plots generated from sequential image pairs and demonstrate their effectiveness on detecting motion while robustly ignoring illumination change. © 2008 SPIE-IS&T.

关键词： image segmentation

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