17 research outputs found
Deep Network for Simultaneous Decomposition and Classification in UWB-SAR Imagery
Classifying buried and obscured targets of interest from other natural and
manmade clutter objects in the scene is an important problem for the U.S. Army.
Targets of interest are often represented by signals captured using
low-frequency (UHF to L-band) ultra-wideband (UWB) synthetic aperture radar
(SAR) technology. This technology has been used in various applications,
including ground penetration and sensing-through-the-wall. However, the
technology still faces a significant issues regarding low-resolution SAR
imagery in this particular frequency band, low radar cross sections (RCS),
small objects compared to radar signal wavelengths, and heavy interference. The
classification problem has been firstly, and partially, addressed by sparse
representation-based classification (SRC) method which can extract noise from
signals and exploit the cross-channel information. Despite providing potential
results, SRC-related methods have drawbacks in representing nonlinear relations
and dealing with larger training sets. In this paper, we propose a Simultaneous
Decomposition and Classification Network (SDCN) to alleviate noise inferences
and enhance classification accuracy. The network contains two jointly trained
sub-networks: the decomposition sub-network handles denoising, while the
classification sub-network discriminates targets from confusers. Experimental
results show significant improvements over a network without decomposition and
SRC-related methods
Orthogonally Regularized Deep Networks For Image Super-resolution
Deep learning methods, in particular trained Convolutional Neural Networks
(CNNs) have recently been shown to produce compelling state-of-the-art results
for single image Super-Resolution (SR). Invariably, a CNN is learned to map the
low resolution (LR) image to its corresponding high resolution (HR) version in
the spatial domain. Aiming for faster inference and more efficient solutions
than solving the SR problem in the spatial domain, we propose a novel network
structure for learning the SR mapping function in an image transform domain,
specifically the Discrete Cosine Transform (DCT). As a first contribution, we
show that DCT can be integrated into the network structure as a Convolutional
DCT (CDCT) layer. We further extend the network to allow the CDCT layer to
become trainable (i.e. optimizable). Because this layer represents an image
transform, we enforce pairwise orthogonality constraints on the individual
basis functions/filters. This Orthogonally Regularized Deep SR network (ORDSR)
simplifies the SR task by taking advantage of image transform domain while
adapting the design of transform basis to the training image set
Deep MR Brain Image Super-Resolution Using Spatio-Structural Priors
High resolution Magnetic Resonance (MR) images are desired for accurate
diagnostics. In practice, image resolution is restricted by factors like
hardware and processing constraints. Recently, deep learning methods have been
shown to produce compelling state-of-the-art results for image
enhancement/super-resolution. Paying particular attention to desired
hi-resolution MR image structure, we propose a new regularized network that
exploits image priors, namely a low-rank structure and a sharpness prior to
enhance deep MR image super-resolution (SR). Our contributions are then
incorporating these priors in an analytically tractable fashion \color{black}
as well as towards a novel prior guided network architecture that accomplishes
the super-resolution task. This is particularly challenging for the low rank
prior since the rank is not a differentiable function of the image matrix(and
hence the network parameters), an issue we address by pursuing differentiable
approximations of the rank. Sharpness is emphasized by the variance of the
Laplacian which we show can be implemented by a fixed feedback layer at the
output of the network. As a key extension, we modify the fixed feedback
(Laplacian) layer by learning a new set of training data driven filters that
are optimized for enhanced sharpness. Experiments performed on publicly
available MR brain image databases and comparisons against existing
state-of-the-art methods show that the proposed prior guided network offers
significant practical gains in terms of improved SNR/image quality measures.
Because our priors are on output images, the proposed method is versatile and
can be combined with a wide variety of existing network architectures to
further enhance their performance.Comment: Accepted to IEEE transactions on Image Processin