5 research outputs found
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
DFDL: Discriminative Feature-oriented Dictionary Learning for Histopathological Image Classification
In histopathological image analysis, feature extraction for classification is
a challenging task due to the diversity of histology features suitable for each
problem as well as presence of rich geometrical structure. In this paper, we
propose an automatic feature discovery framework for extracting discriminative
class-specific features and present a low-complexity method for classification
and disease grading in histopathology. Essentially, our Discriminative
Feature-oriented Dictionary Learning (DFDL) method learns class-specific
features which are suitable for representing samples from the same class while
are poorly capable of representing samples from other classes. Experiments on
three challenging real-world image databases: 1) histopathological images of
intraductal breast lesions, 2) mammalian lung images provided by the Animal
Diagnostics Lab (ADL) at Pennsylvania State University, and 3) brain tumor
images from The Cancer Genome Atlas (TCGA) database, show the significance of
DFDL model in a variety problems over state-of-the-art methodsComment: Accepted to IEEE International Symposium on Biomedical Imaging
(ISBI), 201