5,045 research outputs found
Structured Sparsity Models for Multiparty Speech Recovery from Reverberant Recordings
We tackle the multi-party speech recovery problem through modeling the
acoustic of the reverberant chambers. Our approach exploits structured sparsity
models to perform room modeling and speech recovery. We propose a scheme for
characterizing the room acoustic from the unknown competing speech sources
relying on localization of the early images of the speakers by sparse
approximation of the spatial spectra of the virtual sources in a free-space
model. The images are then clustered exploiting the low-rank structure of the
spectro-temporal components belonging to each source. This enables us to
identify the early support of the room impulse response function and its unique
map to the room geometry. To further tackle the ambiguity of the reflection
ratios, we propose a novel formulation of the reverberation model and estimate
the absorption coefficients through a convex optimization exploiting joint
sparsity model formulated upon spatio-spectral sparsity of concurrent speech
representation. The acoustic parameters are then incorporated for separating
individual speech signals through either structured sparse recovery or inverse
filtering the acoustic channels. The experiments conducted on real data
recordings demonstrate the effectiveness of the proposed approach for
multi-party speech recovery and recognition.Comment: 31 page
A robust nonlinear scale space change detection approach for SAR images
In this paper, we propose a change detection approach based on nonlinear scale space analysis of change images for robust detection of various changes incurred by natural phenomena and/or human activities in Synthetic Aperture Radar (SAR) images using Maximally Stable Extremal Regions (MSERs). To achieve this, a variant of the log-ratio image of multitemporal images is calculated which is followed by Feature Preserving Despeckling (FPD) to generate nonlinear scale space images exhibiting different trade-offs in terms of speckle reduction and shape detail preservation. MSERs of each scale space image are found and then combined through a decision level fusion strategy, namely "selective scale fusion" (SSF), where contrast and boundary curvature of each MSER are considered. The performance of the proposed method is evaluated using real multitemporal high resolution TerraSAR-X images and synthetically generated multitemporal images composed of shapes with several orientations, sizes, and backscatter amplitude levels representing a variety of possible signatures of change. One of the main outcomes of this approach is that different objects having different sizes and levels of contrast with their surroundings appear as stable regions at different scale space images thus the fusion of results from scale space images yields a good overall performance
Distance Measures for Reduced Ordering Based Vector Filters
Reduced ordering based vector filters have proved successful in removing
long-tailed noise from color images while preserving edges and fine image
details. These filters commonly utilize variants of the Minkowski distance to
order the color vectors with the aim of distinguishing between noisy and
noise-free vectors. In this paper, we review various alternative distance
measures and evaluate their performance on a large and diverse set of images
using several effectiveness and efficiency criteria. The results demonstrate
that there are in fact strong alternatives to the popular Minkowski metrics
Fractional biorthogonal partners in channel equalization and signal interpolation
The concept of biorthogonal partners has been introduced recently by the authors. The work presented here is an extension of some of these results to the case where the upsampling and downsampling ratios are not integers but rational numbers, hence, the name fractional biorthogonal partners. The conditions for the existence of stable and of finite impulse response (FIR) fractional biorthogonal partners are derived. It is also shown that the FIR solutions (when they exist) are not unique. This property is further explored in one of the applications of fractional biorthogonal partners, namely, the fractionally spaced equalization in digital communications. The goal is to construct zero-forcing equalizers (ZFEs) that also combat the channel noise. The performance of these equalizers is assessed through computer simulations. Another application considered is the all-FIR interpolation technique with the minimum amount of oversampling required in the input signal. We also consider the extension of the least squares approximation problem to the setting of fractional biorthogonal partners
A Non-Local Structure Tensor Based Approach for Multicomponent Image Recovery Problems
Non-Local Total Variation (NLTV) has emerged as a useful tool in variational
methods for image recovery problems. In this paper, we extend the NLTV-based
regularization to multicomponent images by taking advantage of the Structure
Tensor (ST) resulting from the gradient of a multicomponent image. The proposed
approach allows us to penalize the non-local variations, jointly for the
different components, through various matrix norms with .
To facilitate the choice of the hyper-parameters, we adopt a constrained convex
optimization approach in which we minimize the data fidelity term subject to a
constraint involving the ST-NLTV regularization. The resulting convex
optimization problem is solved with a novel epigraphical projection method.
This formulation can be efficiently implemented thanks to the flexibility
offered by recent primal-dual proximal algorithms. Experiments are carried out
for multispectral and hyperspectral images. The results demonstrate the
interest of introducing a non-local structure tensor regularization and show
that the proposed approach leads to significant improvements in terms of
convergence speed over current state-of-the-art methods
Sub-Nyquist Sampling: Bridging Theory and Practice
Sampling theory encompasses all aspects related to the conversion of
continuous-time signals to discrete streams of numbers. The famous
Shannon-Nyquist theorem has become a landmark in the development of digital
signal processing. In modern applications, an increasingly number of functions
is being pushed forward to sophisticated software algorithms, leaving only
those delicate finely-tuned tasks for the circuit level.
In this paper, we review sampling strategies which target reduction of the
ADC rate below Nyquist. Our survey covers classic works from the early 50's of
the previous century through recent publications from the past several years.
The prime focus is bridging theory and practice, that is to pinpoint the
potential of sub-Nyquist strategies to emerge from the math to the hardware. In
that spirit, we integrate contemporary theoretical viewpoints, which study
signal modeling in a union of subspaces, together with a taste of practical
aspects, namely how the avant-garde modalities boil down to concrete signal
processing systems. Our hope is that this presentation style will attract the
interest of both researchers and engineers in the hope of promoting the
sub-Nyquist premise into practical applications, and encouraging further
research into this exciting new frontier.Comment: 48 pages, 18 figures, to appear in IEEE Signal Processing Magazin
Comparison of modern nonlinear multichannel filtering techniques using recent full-reference image quality assessment methods
In the paper the quality analysis of some modern nonlinear color image filtering methods is presented. Traditionally, many image filtering algorithms are analyzed using classical image quality assessment metrics, mainly based on the Mean Square Error (MSE). However, they are all poorly correlated with subjective evaluation of images performed by observers.Due to necessity of better image quality estimation, some other methods have been recently proposed. They are especially useful for development of new lossy image compression algorithms, as well as evaluation of images obtained after applying some image processing algorithms e.g. filtering methods.Most of image quality algorithms are based on the comparison of similarity between two images: the original (reference) one and the second one which is processed e.g. contaminated by noise, filtered or lossily compressed. Such a group of full-reference methods is actually the only existing universal solution for automatic image quality assessment. There are also some blind (no-reference) algorithms but they are specialized for some kinds of distortions e.g. blocky effects in the JPEG compressed images. The last years' state-of-the-art full-reference metrics are Structural Similarity (SSIM) and M-SVD based on the Singular Value Decomposition of two images' respective blocks.Another important aspect of color image quality assessment is the way the color information is utilized in the quality metric. The authors of two analyzed metrics generally do not consider the effects of using color information at all or limit the usage of their metrics to luminance information in YUV color model only so in this article the solutions based on RGB and CIE LAB models are compared.In the paper the results of quality assessment using the SSIM and M-SVD methods obtained for some modern median-based filters and Distance-Directional Filter for color images are presented with comparison to those obtained using classical metrics as the verification of their usefulness
Comparison of modern nonlinear multichannel filtering techniques using recent full-reference image quality assessment methods
In the paper the quality analysis of some modern nonlinear color image filtering methods is presented. Traditionally, many image filtering algorithms are analyzed using classical image quality assessment metrics, mainly based on the Mean Square Error (MSE). However, they are all poorly correlated with subjective evaluation of images performed by observers.Due to necessity of better image quality estimation, some other methods have been recently proposed. They are especially useful for development of new lossy image compression algorithms, as well as evaluation of images obtained after applying some image processing algorithms e.g. filtering methods.Most of image quality algorithms are based on the comparison of similarity between two images: the original (reference) one and the second one which is processed e.g. contaminated by noise, filtered or lossily compressed. Such a group of full-reference methods is actually the only existing universal solution for automatic image quality assessment. There are also some blind (no-reference) algorithms but they are specialized for some kinds of distortions e.g. blocky effects in the JPEG compressed images. The last years' state-of-the-art full-reference metrics are Structural Similarity (SSIM) and M-SVD based on the Singular Value Decomposition of two images' respective blocks.Another important aspect of color image quality assessment is the way the color information is utilized in the quality metric. The authors of two analyzed metrics generally do not consider the effects of using color information at all or limit the usage of their metrics to luminance information in YUV color model only so in this article the solutions based on RGB and CIE LAB models are compared.In the paper the results of quality assessment using the SSIM and M-SVD methods obtained for some modern median-based filters and Distance-Directional Filter for color images are presented with comparison to those obtained using classical metrics as the verification of their usefulness
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