Current Issue : January - March Volume : 2013 Issue Number : 1 Articles : 5 Articles
Magnetic Resource Force Microscopy for Reconstruction of H-B Sparse Fingerprint Image presents a Hierarchical Bayesian model to reconstruct sparse images when the observations are obtained from linear transformations and corrupted by an additive white Gaussian noise. Our hierarchical Bayes model is well suited to such naturally sparse fingerprint image applications as it seamlessly accounts for properties such as sparsity and positivity of the fingerprint image via appropriate Bayes priors. We propose a prior that is based on a weighted mixture of a positive exponential distribution and a mass at zero. The prior has hyperparameters that are tuned automatically by marginalization over the hierarchical Bayesian model. To overcome the complexity of the posterior distribution, a Gibbs sampling strategy is proposed. The Gibbs samples can be used to estimate the image to be recovered, e.g. by maximizing the estimated posterior distribution. In our fully Bayesian approach the posteriors of all the parameters are available.Gibbs algorithm provides more information than other previously proposed sparse reconstruction methods that only give a point estimate. The performance of our hierarchical Bayesian sparse reconstruction method is illustrated on synthetic and real data collected from a tobacco virus sample using a prototype MRFM instrument....
This article describes a pipeline synthesis and optimization technique that increases data throughput of FPGAbased\r\nsystem using minimum pipeline resources. The technique is applied on CAL dataflow language, and\r\ndesigned based on relations, matrices, and graphs. First, the initial as-soon-as-possible (ASAP) and as-late-aspossible\r\n(ALAP) schedules, and the corresponding mobility of operators are generated. From this, operator coloring\r\ntechnique is used on conflict and nonconflict directed graphs using recursive functions and explicit stack\r\nmechanisms. For each feasible number of pipeline stages, a pipeline schedule with minimum total register width is\r\ntaken as an optimal coloring, which is then automatically transformed to a description in CAL. The generated\r\npipelined CAL descriptions are finally synthesized to hardware description languages for FPGA implementation.\r\nExperimental results of three video processing applications demonstrate up to 3.9Ã?â?? higher throughput for\r\npipelined compared to non-pipelined implementations, and average total pipeline register width reduction of up\r\nto 39.6 and 49.9% between the optimal, and ASAP and ALAP pipeline schedules, respectively....
In this article, a prediction error preprocessor based on the just noticeable distortion (JND) for the color image\r\ncompression scheme is presented. The dynamic range of prediction error signals we can reduce, the lower bit rate\r\nof the reconstructed image we can obtain at high visual quality. We propose a color JND estimator that is\r\nincorporated into the design of the preprocessor in the compression scheme. The color JND estimator is carried\r\nout in the wavelet domain to present good estimates to the available amount masking. The estimated JND is used\r\nto preprocess the signal and is also used to incorporate into the design of the quantization stage in the\r\ncompression scheme for higher performance. Simulation results show that the bit rate required by the\r\ncompression scheme with the preprocessor is lower at high visual quality of the reconstructed color image. The\r\npreprocessor is further applied to the input color image of the JPEG and JPEG2000 coders for better performance....
Low-level computer vision algorithms have high computational requirements. In this study, we present two realtime\r\narchitectures using resource constrained FPGA and GPU devices for the computation of a new algorithm\r\nwhich performs tone mapping, contrast enhancement, and glare mitigation. Our goal is to implement this operator\r\nin a portable and battery-operated device, in order to obtain a low vision aid specially aimed at visually impaired\r\npeople who struggle to manage themselves in environments where illumination is not uniform or changes rapidly.\r\nThis aid device processes in real-time, with minimum latency, the input of a camera and shows the enhanced\r\nimage on a head mounted display (HMD). Therefore, the proposed operator has been implemented on batteryoperated\r\nplatforms, one based on the GPU NVIDIA ION2 and another on the FPGA Spartan III, which perform at\r\nrates of 30 and 60 frames per second, respectively, when working with VGA resolution images (640 Ã?â?? 480)....
Denoising is always a challenging problem in magnetic resonance imaging (MRI) and is important for clinical\r\ndiagnosis and computerized analysis, such as tissue classification and segmentation. The noise in MRI has a Rician\r\ndistribution. Unlike additive Gaussian noise, Rician noise is signal dependent, and separating the signal from the\r\nnoise is a difficult task. In this paper, we propose a useful alternative of the nonlocal mean (NLM) filter that uses\r\nnonparametric principal component analysis (NPCA) for Rician noise reduction in MR images. This alternative is\r\ncalled the NPCA-NLM filter, and it results in improved accuracy and computational performance. We present an\r\napplicable method for estimating smoothing kernel width parameters for a much larger set of images and\r\ndemonstrate that the number of principal components for NPCA is robust to variations in the noise as well as in\r\nimages. Finally, we investigate the performance of the proposed filter with the standard NLM filter and the PCANLM\r\nfilter on MR images corrupted with various levels of Rician noise. The experimental results indicate that the\r\nNPCA-NLM filter is the most robust to variations in images, and shows good performance at all noise levels tested....
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