1,597 research outputs found
Image Compressive Sensing Recovery Using Adaptively Learned Sparsifying Basis via L0 Minimization
From many fewer acquired measurements than suggested by the Nyquist sampling
theory, compressive sensing (CS) theory demonstrates that, a signal can be
reconstructed with high probability when it exhibits sparsity in some domain.
Most of the conventional CS recovery approaches, however, exploited a set of
fixed bases (e.g. DCT, wavelet and gradient domain) for the entirety of a
signal, which are irrespective of the non-stationarity of natural signals and
cannot achieve high enough degree of sparsity, thus resulting in poor CS
recovery performance. In this paper, we propose a new framework for image
compressive sensing recovery using adaptively learned sparsifying basis via L0
minimization. The intrinsic sparsity of natural images is enforced
substantially by sparsely representing overlapped image patches using the
adaptively learned sparsifying basis in the form of L0 norm, greatly reducing
blocking artifacts and confining the CS solution space. To make our proposed
scheme tractable and robust, a split Bregman iteration based technique is
developed to solve the non-convex L0 minimization problem efficiently.
Experimental results on a wide range of natural images for CS recovery have
shown that our proposed algorithm achieves significant performance improvements
over many current state-of-the-art schemes and exhibits good convergence
property.Comment: 31 pages, 4 tables, 12 figures, to be published at Signal Processing,
Code available: http://idm.pku.edu.cn/staff/zhangjian/ALSB
Geo-tagging and privacy-preservation in mobile cloud computing
With the emerge of the cloud computing service and the explosive growth of the mobile devices and applications, mobile computing technologies and cloud computing technologies have been drawing significant attentions. Mobile cloud computing, with the synergy between the cloud and mobile technologies, has brought us new opportunities to develop novel and practical systems such as mobile multimedia systems and cloud systems that provide collaborative data-mining services for data from disparate owners (e.g., mobile users). However, it also creates new challenges, e.g., the algorithms deployed in the computationally weak mobile device require higher efficiency, and introduces new problems such as the privacy concern when the private data is shared in the cloud for collaborative data-mining. The main objectives of this dissertation are: 1. to develop practical systems based on the unique features of mobile devices (i.e., all-in-one computing platform and sensors) and the powerful computing capability of the cloud; 2. to propose solutions protecting the data privacy when the data from disparate owners are shared in the cloud for collaborative data-mining. We first propose a mobile geo-tagging system. It is a novel, accurate and efficient image and video based remote target localization and tracking system using the Android smartphone. To cope with the smartphones' computational limitation, we design light-weight image/video processing algorithms to achieve a good balance between estimation accuracy and computational complexity. Our system is first of its kind and we provide first hand real-world experimental results, which demonstrate that our system is feasible and practicable. To address the privacy concern when data from disparate owners are shared in the cloud for collaborative data-mining, we then propose a generic compressive sensing (CS) based secure multiparty computation (MPC) framework for privacy-preserving collaborative data-mining in which data mining is performed in the CS domain. We perform the CS transformation and reconstruction processes with MPC protocols. We modify the original orthogonal matching pursuit algorithm and develop new MPC protocols so that the CS reconstruction process can be implemented using MPC. Our analysis and experimental results show that our generic framework is capable of enabling privacy preserving collaborative data-mining. The proposed framework can be applied to many privacy preserving collaborative data-mining and signal processing applications in the cloud. We identify an application scenario that requires simultaneously performing secure watermark detection and privacy preserving multimedia data storage. We further propose a privacy preserving storage and secure watermark detection framework by adopting our generic framework to address such a requirement. In our secure watermark detection framework, the multimedia data and secret watermark pattern are presented to the cloud for secure watermark detection in a compressive sensing domain to protect the privacy. We also give mathematical and statistical analysis to derive the expected watermark detection performance in the compressive sensing domain, based on the target image, watermark pattern and the size of the compressive sensing matrix (but without the actual CS matrix), which means that the watermark detection performance in the CS domain can be estimated during the watermark embedding process. The correctness of the derived performance has been validated by our experiments. Our theoretical analysis and experimental results show that secure watermark detection in the compressive sensing domain is feasible. By taking advantage of our mobile geo-tagging system and compressive sensing based privacy preserving data-mining framework, we develop a mobile privacy preserving collaborative filtering system. In our system, mobile users can share their personal data with each other in the cloud and get daily activity recommendations based on the data-mining results generated by the cloud, without leaking the privacy and secrecy of the data to other parties. Experimental results demonstrate that the proposed system is effective in enabling efficient mobile privacy preserving collaborative filtering services.Includes bibliographical references (pages 126-133)
Hyperspectral Unmixing Overview: Geometrical, Statistical, and Sparse Regression-Based Approaches
Imaging spectrometers measure electromagnetic energy scattered in their
instantaneous field view in hundreds or thousands of spectral channels with
higher spectral resolution than multispectral cameras. Imaging spectrometers
are therefore often referred to as hyperspectral cameras (HSCs). Higher
spectral resolution enables material identification via spectroscopic analysis,
which facilitates countless applications that require identifying materials in
scenarios unsuitable for classical spectroscopic analysis. Due to low spatial
resolution of HSCs, microscopic material mixing, and multiple scattering,
spectra measured by HSCs are mixtures of spectra of materials in a scene. Thus,
accurate estimation requires unmixing. Pixels are assumed to be mixtures of a
few materials, called endmembers. Unmixing involves estimating all or some of:
the number of endmembers, their spectral signatures, and their abundances at
each pixel. Unmixing is a challenging, ill-posed inverse problem because of
model inaccuracies, observation noise, environmental conditions, endmember
variability, and data set size. Researchers have devised and investigated many
models searching for robust, stable, tractable, and accurate unmixing
algorithms. This paper presents an overview of unmixing methods from the time
of Keshava and Mustard's unmixing tutorial [1] to the present. Mixing models
are first discussed. Signal-subspace, geometrical, statistical, sparsity-based,
and spatial-contextual unmixing algorithms are described. Mathematical problems
and potential solutions are described. Algorithm characteristics are
illustrated experimentally.Comment: This work has been accepted for publication in IEEE Journal of
Selected Topics in Applied Earth Observations and Remote Sensin
Probabilistic-based Feature Embedding of 4-D Light Fields for Compressive Imaging and Denoising
The high-dimensional nature of the 4-D light field (LF) poses great
challenges in achieving efficient and effective feature embedding, that
severely impacts the performance of downstream tasks. To tackle this crucial
issue, in contrast to existing methods with empirically-designed architectures,
we propose a probabilistic-based feature embedding (PFE), which learns a
feature embedding architecture by assembling various low-dimensional
convolution patterns in a probability space for fully capturing spatial-angular
information. Building upon the proposed PFE, we then leverage the intrinsic
linear imaging model of the coded aperture camera to construct a
cycle-consistent 4-D LF reconstruction network from coded measurements.
Moreover, we incorporate PFE into an iterative optimization framework for 4-D
LF denoising. Our extensive experiments demonstrate the significant superiority
of our methods on both real-world and synthetic 4-D LF images, both
quantitatively and qualitatively, when compared with state-of-the-art methods.
The source code will be publicly available at
https://github.com/lyuxianqiang/LFCA-CR-NET
Depth Super-Resolution with Hybrid Camera System
An important field of research in computer vision is the 3D analysis and reconstruction of objects and scenes. Currently, among all the the techniques for 3D acquisition, stereo vision systems are the most common. More recently, Time-of-Flight (ToF) range cameras have been introduced. The focus of this thesis is to combine the information from the ToF with one or two standard cameras, in order to obtain a high- resolution depth imageopenEmbargo per motivi di segretezza e/o di proprietà dei risultati e informazioni di enti esterni o aziende private che hanno partecipato alla realizzazione del lavoro di ricerca relativo alla tes
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