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Compressive sensing (CS) is a technique that enables the recovery of sparse signals using fewer measurements than traditional sampling methods. To address the computational challenges of CS reconstruction, our objective is to develop an interpretable and concise neural network model for reconstructing natural images using CS. We achieve this by mapping one step of the iterative shrinkage thresholding algorithm (ISTA) to a deep network block, representing one iteration of ISTA. To enhance learning ability and incorporate structural diversity, we integrate aggregated residual transformations (ResNeXt) and squeeze-and-excitation mechanisms into the ISTA block. This block serves as a deep equilibrium layer connected to a semi-tensor product network for convenient sampling and providing an initial reconstruction. The resulting model, called MsDC-DEQ-Net, exhibits competitive performance compared to state-of-the-art network-based methods. It significantly reduces storage requirements compared to deep unrolling methods, using only one iteration block instead of multiple iterations. Unlike deep unrolling models, MsDC-DEQ-Net can be iteratively used, gradually improving reconstruction accuracy while considering computation tradeoffs. Additionally, the model benefits from multiscale dilated convolutions, further enhancing performance. [ABSTRACT FROM AUTHOR]

We present a new technique for separating two speech signals from a single recording. The proposed method bridges the gap between underdetermined blind source separation techniques and those techniques that model the human auditory system, that is, computational auditory scene analysis (CASA). For this purpose, we decompose the speech signal into the excitation signal and the vocal-tract-related filter and then estimate the components from the mixed speech using a hybrid model. We first express the probability density function (PDF) of the mixed speech's log spectral vectors in terms of the PDFs of the underlying speech signal's vocal-tract-related filters. Then, the mean vectors of PDFs of the vocal-tract-related filters are obtained using a maximum likelihood estimator given the mixed signal. Finally, the estimated vocal-tract-related filters along with the extracted fundamental frequencies are used to reconstruct estimates of the individual speech signals. The proposed technique effectively adds vocal-tract-related filter characteristics as a new cue to CASA models using a new grouping technique based on an underdetermined blind source separation. We compare our model with both an underdetermined blind source separation and a CASA method. The experimental results show that our model outperforms both techniques in terms of SNR improvement and the percentage of crosstalk suppression.

We present an image registration model for image sets with arbitrarily shaped local illumination variations between images. Any nongeometric variations tend to degrade the geometric registration precision and impact subsequent processing. Traditional image registration approaches do not typically account for changes and movement of light sources, which result in interimage illumination differences with arbitrary shape. In addition, these approaches typically use a least-square estimator that is sensitive to outliers, where interimage illumination variations are often large enough to act as outliers. In this paper, we propose an image registration approach that compensates for arbitrarily shaped interimage illumination variations, which are processed using robust M-estimators tuned to that region. Each M-estimator for each illumination region has a distinct cost function by which small and large interimage residuals are unevenly penalized. Since the segmentation of the interimage illumination variations may not be perfect, a segmentation confidence weighting is also imposed to reduce the negative effect of mis-segmentation around illumination region boundaries. The proposed approach is cast in an iterative coarse-to-fine framework, which allows a convergence rate similar to competing intensity-based image registration approaches. The overall proposed approach is presented in a general framework, but experimental results use the bisquare M-estimator with region segmentation confidence weighting. A nearly tenfold improvement in subpixel registration precision is seen with the proposed technique when convergence is attained, as compared with competing techniques using both simulated and real data sets with interimage illumination variations. [ABSTRACT FROM AUTHOR]

This paper proposes a new method for performing joint audio-video talker localization that explores the reliability of the individual localization estimates such as audio, motion detection, and skin-color detection. The reliability information is estimated from the audio and video data separately. The proposed method then uses this reliability information in conjunction with a simple summing voter to dynamically discriminate erroneous outputs from the localizers while performing fusion on the localization results. Based on the voter output, a majority rule is then used to make the final decision of the active talker's current location. The results show that adding the reliability information during fusion improves localization performance when compared to audio only, motion detection only, skin-color detection only, and joint audio-video using straight summing fusion localization methods. The computational complexity of the proposed method is comparable to the existing ones. [ABSTRACT FROM AUTHOR]