1,562 research outputs found
Adaptive Measurement Network for CS Image Reconstruction
Conventional compressive sensing (CS) reconstruction is very slow for its
characteristic of solving an optimization problem. Convolu- tional neural
network can realize fast processing while achieving compa- rable results. While
CS image recovery with high quality not only de- pends on good reconstruction
algorithms, but also good measurements. In this paper, we propose an adaptive
measurement network in which measurement is obtained by learning. The new
network consists of a fully-connected layer and ReconNet. The fully-connected
layer which has low-dimension output acts as measurement. We train the
fully-connected layer and ReconNet simultaneously and obtain adaptive
measurement. Because the adaptive measurement fits dataset better, in contrast
with random Gaussian measurement matrix, under the same measuremen- t rate, it
can extract the information of scene more efficiently and get better
reconstruction results. Experiments show that the new network outperforms the
original one.Comment: 11pages,8figure
ISTA-Net: Interpretable Optimization-Inspired Deep Network for Image Compressive Sensing
With the aim of developing a fast yet accurate algorithm for compressive
sensing (CS) reconstruction of natural images, we combine in this paper the
merits of two existing categories of CS methods: the structure insights of
traditional optimization-based methods and the speed of recent network-based
ones. Specifically, we propose a novel structured deep network, dubbed
ISTA-Net, which is inspired by the Iterative Shrinkage-Thresholding Algorithm
(ISTA) for optimizing a general norm CS reconstruction model. To cast
ISTA into deep network form, we develop an effective strategy to solve the
proximal mapping associated with the sparsity-inducing regularizer using
nonlinear transforms. All the parameters in ISTA-Net (\eg nonlinear transforms,
shrinkage thresholds, step sizes, etc.) are learned end-to-end, rather than
being hand-crafted. Moreover, considering that the residuals of natural images
are more compressible, an enhanced version of ISTA-Net in the residual domain,
dubbed {ISTA-Net}, is derived to further improve CS reconstruction.
Extensive CS experiments demonstrate that the proposed ISTA-Nets outperform
existing state-of-the-art optimization-based and network-based CS methods by
large margins, while maintaining fast computational speed. Our source codes are
available: \textsl{http://jianzhang.tech/projects/ISTA-Net}.Comment: 10 pages, 6 figures, 4 Tables. To appear in CVPR 201
Compressively Sensed Image Recognition
Compressive Sensing (CS) theory asserts that sparse signal reconstruction is
possible from a small number of linear measurements. Although CS enables
low-cost linear sampling, it requires non-linear and costly reconstruction.
Recent literature works show that compressive image classification is possible
in CS domain without reconstruction of the signal. In this work, we introduce a
DCT base method that extracts binary discriminative features directly from CS
measurements. These CS measurements can be obtained by using (i) a random or a
pseudo-random measurement matrix, or (ii) a measurement matrix whose elements
are learned from the training data to optimize the given classification task.
We further introduce feature fusion by concatenating Bag of Words (BoW)
representation of our binary features with one of the two state-of-the-art
CNN-based feature vectors. We show that our fused feature outperforms the
state-of-the-art in both cases.Comment: 6 pages, submitted/accepted, EUVIP 201
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