14 research outputs found
Adaptive-Rate Compressive Sensing Using Side Information
We provide two novel adaptive-rate compressive sensing (CS) strategies for
sparse, time-varying signals using side information. Our first method utilizes
extra cross-validation measurements, and the second one exploits extra
low-resolution measurements. Unlike the majority of current CS techniques, we
do not assume that we know an upper bound on the number of significant
coefficients that comprise the images in the video sequence. Instead, we use
the side information to predict the number of significant coefficients in the
signal at the next time instant. For each image in the video sequence, our
techniques specify a fixed number of spatially-multiplexed CS measurements to
acquire, and adjust this quantity from image to image. Our strategies are
developed in the specific context of background subtraction for surveillance
video, and we experimentally validate the proposed methods on real video
sequences
Structured Sparse Modelling with Hierarchical GP
In this paper a new Bayesian model for sparse linear regression with a
spatio-temporal structure is proposed. It incorporates the structural
assumptions based on a hierarchical Gaussian process prior for spike and slab
coefficients. We design an inference algorithm based on Expectation Propagation
and evaluate the model over the real data.Comment: SPARS 201
Structured Sparse Modelling with Hierarchical GP
In this paper a new Bayesian model for sparse linear regression with a spatio-temporal structure is proposed. It incorporates the structural assumptions based on a hierarchical Gaussian process prior for spike and slab coefficients. We design an inference algorithm based on Expectation Propagation and evaluate the model over the real data
Structured Sparse Modelling with Hierarchical GP
In this paper a new Bayesian model for sparse linear regression with a spatio-temporal structure is proposed. It incorporates the structural assumptions based on a hierarchical Gaussian process prior for spike and slab coefficients. We design an inference algorithm based on Expectation Propagation and evaluate the model over the real data
Multi-Channel Deep Networks for Block-Based Image Compressive Sensing
Incorporating deep neural networks in image compressive sensing (CS) receives
intensive attentions recently. As deep network approaches learn the inverse
mapping directly from the CS measurements, a number of models have to be
trained, each of which corresponds to a sampling rate. This may potentially
degrade the performance of image CS, especially when multiple sampling rates
are assigned to different blocks within an image. In this paper, we develop a
multi-channel deep network for block-based image CS with performance
significantly exceeding the current state-of-the-art methods. The significant
performance improvement of the model is attributed to block-based sampling
rates allocation and model-level removal of blocking artifacts. Specifically,
the image blocks with a variety of sampling rates can be reconstructed in a
single model by exploiting inter-block correlation. At the same time, the
initially reconstructed blocks are reassembled into a full image to remove
blocking artifacts within the network by unrolling a hand-designed block-based
CS algorithm. Experimental results demonstrate that the proposed method
outperforms the state-of-the-art CS methods by a large margin in terms of
objective metrics, PSNR, SSIM, and subjective visual quality.Comment: 12 pages, 8 figure