3,403 research outputs found
Feasibility study for a scanning celestial attitude determination system SCADS on the IMP spacecraft Final report
System design analysis to establish feasibility of using electro-optical celestial scanning sensor on IMP spacecraft for determination of spacecraft attitude by star measurement
Convolutional Deblurring for Natural Imaging
In this paper, we propose a novel design of image deblurring in the form of
one-shot convolution filtering that can directly convolve with naturally
blurred images for restoration. The problem of optical blurring is a common
disadvantage to many imaging applications that suffer from optical
imperfections. Despite numerous deconvolution methods that blindly estimate
blurring in either inclusive or exclusive forms, they are practically
challenging due to high computational cost and low image reconstruction
quality. Both conditions of high accuracy and high speed are prerequisites for
high-throughput imaging platforms in digital archiving. In such platforms,
deblurring is required after image acquisition before being stored, previewed,
or processed for high-level interpretation. Therefore, on-the-fly correction of
such images is important to avoid possible time delays, mitigate computational
expenses, and increase image perception quality. We bridge this gap by
synthesizing a deconvolution kernel as a linear combination of Finite Impulse
Response (FIR) even-derivative filters that can be directly convolved with
blurry input images to boost the frequency fall-off of the Point Spread
Function (PSF) associated with the optical blur. We employ a Gaussian low-pass
filter to decouple the image denoising problem for image edge deblurring.
Furthermore, we propose a blind approach to estimate the PSF statistics for two
Gaussian and Laplacian models that are common in many imaging pipelines.
Thorough experiments are designed to test and validate the efficiency of the
proposed method using 2054 naturally blurred images across six imaging
applications and seven state-of-the-art deconvolution methods.Comment: 15 pages, for publication in IEEE Transaction Image Processin
UFPR-Periocular: A Periocular Dataset Collected by Mobile Devices in Unconstrained Scenarios
Recently, ocular biometrics in unconstrained environments using images
obtained at visible wavelength have gained the researchers' attention,
especially with images captured by mobile devices. Periocular recognition has
been demonstrated to be an alternative when the iris trait is not available due
to occlusions or low image resolution. However, the periocular trait does not
have the high uniqueness presented in the iris trait. Thus, the use of datasets
containing many subjects is essential to assess biometric systems' capacity to
extract discriminating information from the periocular region. Also, to address
the within-class variability caused by lighting and attributes in the
periocular region, it is of paramount importance to use datasets with images of
the same subject captured in distinct sessions. As the datasets available in
the literature do not present all these factors, in this work, we present a new
periocular dataset containing samples from 1,122 subjects, acquired in 3
sessions by 196 different mobile devices. The images were captured under
unconstrained environments with just a single instruction to the participants:
to place their eyes on a region of interest. We also performed an extensive
benchmark with several Convolutional Neural Network (CNN) architectures and
models that have been employed in state-of-the-art approaches based on
Multi-class Classification, Multitask Learning, Pairwise Filters Network, and
Siamese Network. The results achieved in the closed- and open-world protocol,
considering the identification and verification tasks, show that this area
still needs research and development
Image Restoration for Remote Sensing: Overview and Toolbox
Remote sensing provides valuable information about objects or areas from a
distance in either active (e.g., RADAR and LiDAR) or passive (e.g.,
multispectral and hyperspectral) modes. The quality of data acquired by
remotely sensed imaging sensors (both active and passive) is often degraded by
a variety of noise types and artifacts. Image restoration, which is a vibrant
field of research in the remote sensing community, is the task of recovering
the true unknown image from the degraded observed image. Each imaging sensor
induces unique noise types and artifacts into the observed image. This fact has
led to the expansion of restoration techniques in different paths according to
each sensor type. This review paper brings together the advances of image
restoration techniques with particular focuses on synthetic aperture radar and
hyperspectral images as the most active sub-fields of image restoration in the
remote sensing community. We, therefore, provide a comprehensive,
discipline-specific starting point for researchers at different levels (i.e.,
students, researchers, and senior researchers) willing to investigate the
vibrant topic of data restoration by supplying sufficient detail and
references. Additionally, this review paper accompanies a toolbox to provide a
platform to encourage interested students and researchers in the field to
further explore the restoration techniques and fast-forward the community. The
toolboxes are provided in https://github.com/ImageRestorationToolbox.Comment: This paper is under review in GRS
Panako: a scalable acoustic fingerprinting system handling time-scale and pitch modification
In this paper a scalable granular acoustic fingerprinting system robust against time and pitch scale modification is presented. The aim of acoustic fingerprinting is to identify identical, or recognize similar, audio fragments in a large set using condensed representations of audio signals, i.e. fingerprints. A robust fingerprinting system generates similar fingerprints for perceptually similar audio signals. The new system, presented here, handles a variety of distortions well. It is designed to be robust against pitch shifting, time stretching and tempo changes, while remaining scalable. After a query, the system returns the start time in the reference audio, and the amount of pitch shift and tempo change that has been applied. The design of the system that offers this unique combination of features is the main contribution of this research. The fingerprint itself consists of a combination of key points in a Constant-Q spectrogram. The system is evaluated on commodity hardware using a freely available reference database with fingerprints of over 30.000 songs. The results show that the system responds quickly and reliably on queries, while handling time and pitch scale modifications of up to ten percent
Blur Identification Based on Higher Order Spectral Nulls
The identification of the point spread function (PSF) from the degraded image data constitutes an important first step in image restoration that is known as blur identification. Though a number of blur identification algorithms have been developed in recent years, two of the earlier methods based on the power spectrum and power cepstrum remain popular, because they are easy to implement and have proved to be effective in practical situations. Both methods are limited to PSF\u27s which exhibit spectral nulls, such as due to defocused lens and linear motion blur. Another limitation of these methods is the degradation of their performance in the presence of observation noise. The central slice of the power bispectrum has been employed as an alternative to the power spectrum which can suppress the effects of additive Gaussian noise. In this paper, we utilize the bicepstrum for the identification of linear motion and defocus blurs. We present simulation results where the performance of the blur identification methods based on the spectrum, the cepstrum, the bispectrum and the bicepstrum is compared for different blur sizes and signal-to-noise ratio levels
Supernova / Acceleration Probe: A Satellite Experiment to Study the Nature of the Dark Energy
The Supernova / Acceleration Probe (SNAP) is a proposed space-based
experiment designed to study the dark energy and alternative explanations of
the acceleration of the Universe's expansion by performing a series of
complementary systematics-controlled measurements. We describe a
self-consistent reference mission design for building a Type Ia supernova
Hubble diagram and for performing a wide-area weak gravitational lensing study.
A 2-m wide-field telescope feeds a focal plane consisting of a 0.7
square-degree imager tiled with equal areas of optical CCDs and near infrared
sensors, and a high-efficiency low-resolution integral field spectrograph. The
SNAP mission will obtain high-signal-to-noise calibrated light-curves and
spectra for several thousand supernovae at redshifts between z=0.1 and 1.7. A
wide-field survey covering one thousand square degrees resolves ~100 galaxies
per square arcminute. If we assume we live in a cosmological-constant-dominated
Universe, the matter density, dark energy density, and flatness of space can
all be measured with SNAP supernova and weak-lensing measurements to a
systematics-limited accuracy of 1%. For a flat universe, the
density-to-pressure ratio of dark energy can be similarly measured to 5% for
the present value w0 and ~0.1 for the time variation w'. The large survey area,
depth, spatial resolution, time-sampling, and nine-band optical to NIR
photometry will support additional independent and/or complementary dark-energy
measurement approaches as well as a broad range of auxiliary science programs.
(Abridged)Comment: 40 pages, 18 figures, submitted to PASP, http://snap.lbl.go
- …