6 research outputs found
Representation Learning via Cauchy Convolutional Sparse Coding
In representation learning, Convolutional Sparse Coding (CSC) enables
unsupervised learning of features by jointly optimising both an -norm
fidelity term and a sparsity enforcing penalty. This work investigates using a
regularisation term derived from an assumed Cauchy prior for the coefficients
of the feature maps of a CSC generative model. The sparsity penalty term
resulting from this prior is solved via its proximal operator, which is then
applied iteratively, element-wise, on the coefficients of the feature maps to
optimise the CSC cost function. The performance of the proposed Iterative
Cauchy Thresholding (ICT) algorithm in reconstructing natural images is
compared against the common choice of -norm optimised via soft and
hard thresholding. ICT outperforms IHT and IST in most of these reconstruction
experiments across various datasets, with an average PSNR of up to 11.30 and
7.04 above ISTA and IHT respectively.Comment: 19 pages, 9 figures, journal draf
Representation Learning via Cauchy Convolutional Sparse Coding
In representation learning, Convolutional Sparse Coding (CSC) enables
unsupervised learning of features by jointly optimising both an -norm
fidelity term and a sparsity enforcing penalty. This work investigates using a
regularisation term derived from an assumed Cauchy prior for the coefficients
of the feature maps of a CSC generative model. The sparsity penalty term
resulting from this prior is solved via its proximal operator, which is then
applied iteratively, element-wise, on the coefficients of the feature maps to
optimise the CSC cost function. The performance of the proposed Iterative
Cauchy Thresholding (ICT) algorithm in reconstructing natural images is
compared against the common choice of -norm optimised via soft and
hard thresholding. ICT outperforms IHT and IST in most of these reconstruction
experiments across various datasets, with an average PSNR of up to 11.30 and
7.04 above ISTA and IHT respectively.Comment: 19 pages, 9 figures, journal draf
On Solving SAR Imaging Inverse Problems Using Non-Convex Regularization with a Cauchy-based Penalty
Synthetic aperture radar (SAR) imagery can provide useful information in a
multitude of applications, including climate change, environmental monitoring,
meteorology, high dimensional mapping, ship monitoring, or planetary
exploration. In this paper, we investigate solutions to a number of inverse
problems encountered in SAR imaging. We propose a convex proximal splitting
method for the optimization of a cost function that includes a non-convex
Cauchy-based penalty. The convergence of the overall cost function optimization
is ensured through careful selection of model parameters within a
forward-backward (FB) algorithm. The performance of the proposed penalty
function is evaluated by solving three standard SAR imaging inverse problems,
including super-resolution, image formation, and despeckling, as well as ship
wake detection for maritime applications. The proposed method is compared to
several methods employing classical penalty functions such as total variation
() and norms, and to the generalized minimax-concave (GMC) penalty.
We show that the proposed Cauchy-based penalty function leads to better image
reconstruction results when compared to the reference penalty functions for all
SAR imaging inverse problems in this paper.Comment: 18 pages, 7 figure
A self-paced learning algorithm for change detection in synthetic aperture radar images
Detecting changed regions between two given synthetic aperture radar images is very important to monitor the change of landscapes, change of ecosystem and so on. This can be formulated as a classification problem and addressed by learning a classifier, traditional machine learning classification methods very easily stick to local optima which can be caused by noises of data. Hence, we propose an unsupervised algorithm aiming at constructing a classifier based on self-paced learning. Self-paced learning is a recently developed supervised learning approach and
has been proven to be capable to overcome effectively this shortcoming. After applying a pre-classification to the difference image, we uniformly select samples using the initial result. Then, self-paced learning is utilized to train a classifier. Finally, a filter is used based on spatial contextual information to further smooth the classification result. In order to demonstrate the efficiency of the proposed algorithm, we apply our proposed algorithm on five real synthetic aperture radar images datasets. The results obtained by our algorithm are compared with five other state-of-the-art algorithms, which demonstrates that our algorithm outperforms those state-of-the-art algorithms in terms of accuracy and robustness