481 research outputs found
Deep Networks for Compressed Image Sensing
The compressed sensing (CS) theory has been successfully applied to image
compression in the past few years as most image signals are sparse in a certain
domain. Several CS reconstruction models have been recently proposed and
obtained superior performance. However, there still exist two important
challenges within the CS theory. The first one is how to design a sampling
mechanism to achieve an optimal sampling efficiency, and the second one is how
to perform the reconstruction to get the highest quality to achieve an optimal
signal recovery. In this paper, we try to deal with these two problems with a
deep network. First of all, we train a sampling matrix via the network training
instead of using a traditional manually designed one, which is much appropriate
for our deep network based reconstruct process. Then, we propose a deep network
to recover the image, which imitates traditional compressed sensing
reconstruction processes. Experimental results demonstrate that our deep
networks based CS reconstruction method offers a very significant quality
improvement compared against state of the art ones.Comment: This paper has been accepted by the IEEE International Conference on
Multimedia and Expo (ICME) 201
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Fast Hierarchical Deep Unfolding Network for Image Compressed Sensing
By integrating certain optimization solvers with deep neural network, deep
unfolding network (DUN) has attracted much attention in recent years for image
compressed sensing (CS). However, there still exist several issues in existing
DUNs: 1) For each iteration, a simple stacked convolutional network is usually
adopted, which apparently limits the expressiveness of these models. 2) Once
the training is completed, most hyperparameters of existing DUNs are fixed for
any input content, which significantly weakens their adaptability. In this
paper, by unfolding the Fast Iterative Shrinkage-Thresholding Algorithm
(FISTA), a novel fast hierarchical DUN, dubbed FHDUN, is proposed for image
compressed sensing, in which a well-designed hierarchical unfolding
architecture is developed to cooperatively explore richer contextual prior
information in multi-scale spaces. To further enhance the adaptability, series
of hyperparametric generation networks are developed in our framework to
dynamically produce the corresponding optimal hyperparameters according to the
input content. Furthermore, due to the accelerated policy in FISTA, the newly
embedded acceleration module makes the proposed FHDUN save more than 50% of the
iterative loops against recent DUNs. Extensive CS experiments manifest that the
proposed FHDUN outperforms existing state-of-the-art CS methods, while
maintaining fewer iterations.Comment: Accepted by ACM MM 202
Computational Methods for Matrix/Tensor Factorization and Deep Learning Image Denoising
Feature learning is a technique to automatically extract features from raw data. It is widely used in areas such as computer vision, image processing, data mining and natural language processing. In this thesis, we are interested in the computational aspects of feature learning. We focus on rank matrix and tensor factorization and deep neural network models for image denoising.
With respect to matrix and tensor factorization, we first present a technique to speed up alternating least squares (ALS) and gradient descent (GD) − two commonly used strategies for tensor factorization. We introduce an efficient, scalable and distributed algorithm that addresses the data explosion problem. Instead of a computationally challenging sub-step of ALS and GD, we implement the algorithm on parallel machines by using only two sparse matrix-vector products. Not only is the algorithm scalable but it is also on average 4 to 10 times faster than competing algorithms on various data sets. Next, we discuss our results of non-negative matrix factorization for hyperspectral image data in the presence of noise. We introduce a spectral total variation regularization and derive four variants of the alternating direction method of multiplier algorithm. While all four methods belong to the same family of algorithms, some perform better than others. Thus, we compare the algorithms using stimulated Raman spectroscopic image will be demonstrated.
For deep neural network models, we focus on its application to image denoising. We first demonstrate how an optimal procedure leveraging deep neural networks and convex optimization can combine a given set of denoisers to produce an overall better result. The proposed framework estimates the mean squared error (MSE) of individual denoised outputs using a deep neural network; optimally combines the denoised outputs via convex optimization; and recovers lost details of the combined images using another deep neural network. The framework consistently improves denoising performance for both deterministic denoisers and neural network denoisers. Next, we apply the deep neural network to solve the image reconstruction issues of the Quanta Image Sensor (QIS), which is a single-photon image sensor that oversamples the light field to generate binary measures
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