5,864 research outputs found
Medical image denoising using convolutional denoising autoencoders
Image denoising is an important pre-processing step in medical image
analysis. Different algorithms have been proposed in past three decades with
varying denoising performances. More recently, having outperformed all
conventional methods, deep learning based models have shown a great promise.
These methods are however limited for requirement of large training sample size
and high computational costs. In this paper we show that using small sample
size, denoising autoencoders constructed using convolutional layers can be used
for efficient denoising of medical images. Heterogeneous images can be combined
to boost sample size for increased denoising performance. Simplest of networks
can reconstruct images with corruption levels so high that noise and signal are
not differentiable to human eye.Comment: To appear: 6 pages, paper to be published at the Fourth Workshop on
Data Mining in Biomedical Informatics and Healthcare at ICDM, 201
Convolutional Dictionary Regularizers for Tomographic Inversion
There has been a growing interest in the use of data-driven regularizers to
solve inverse problems associated with computational imaging systems. The
convolutional sparse representation model has recently gained attention, driven
by the development of fast algorithms for solving the dictionary learning and
sparse coding problems for sufficiently large images and data sets.
Nevertheless, this model has seen very limited application to tomographic
reconstruction problems. In this paper, we present a model-based tomographic
reconstruction algorithm using a learnt convolutional dictionary as a
regularizer. The key contribution is the use of a data-dependent weighting
scheme for the l1 regularization to construct an effective denoising method
that is integrated into the inversion using the Plug-and-Play reconstruction
framework. Using simulated data sets we demonstrate that our approach can
improve performance over traditional regularizers based on a Markov random
field model and a patch-based sparse representation model for sparse and
limited-view tomographic data sets
Multi-modal Image Processing based on Coupled Dictionary Learning
In real-world scenarios, many data processing problems often involve
heterogeneous images associated with different imaging modalities. Since these
multimodal images originate from the same phenomenon, it is realistic to assume
that they share common attributes or characteristics. In this paper, we propose
a multi-modal image processing framework based on coupled dictionary learning
to capture similarities and disparities between different image modalities. In
particular, our framework can capture favorable structure similarities across
different image modalities such as edges, corners, and other elementary
primitives in a learned sparse transform domain, instead of the original pixel
domain, that can be used to improve a number of image processing tasks such as
denoising, inpainting, or super-resolution. Practical experiments demonstrate
that incorporating multimodal information using our framework brings notable
benefits.Comment: SPAWC 2018, 19th IEEE International Workshop On Signal Processing
Advances In Wireless Communication
Learning sparse representations of depth
This paper introduces a new method for learning and inferring sparse
representations of depth (disparity) maps. The proposed algorithm relaxes the
usual assumption of the stationary noise model in sparse coding. This enables
learning from data corrupted with spatially varying noise or uncertainty,
typically obtained by laser range scanners or structured light depth cameras.
Sparse representations are learned from the Middlebury database disparity maps
and then exploited in a two-layer graphical model for inferring depth from
stereo, by including a sparsity prior on the learned features. Since they
capture higher-order dependencies in the depth structure, these priors can
complement smoothness priors commonly used in depth inference based on Markov
Random Field (MRF) models. Inference on the proposed graph is achieved using an
alternating iterative optimization technique, where the first layer is solved
using an existing MRF-based stereo matching algorithm, then held fixed as the
second layer is solved using the proposed non-stationary sparse coding
algorithm. This leads to a general method for improving solutions of state of
the art MRF-based depth estimation algorithms. Our experimental results first
show that depth inference using learned representations leads to state of the
art denoising of depth maps obtained from laser range scanners and a time of
flight camera. Furthermore, we show that adding sparse priors improves the
results of two depth estimation methods: the classical graph cut algorithm by
Boykov et al. and the more recent algorithm of Woodford et al.Comment: 12 page
- …