12,521 research outputs found
Spatial deconvolution of spectropolarimetric data: an application to quiet Sun magnetic elements
Observations of the Sun from the Earth are always limited by the presence of
the atmosphere, which strongly disturbs the images. A solution to this problem
is to place the telescopes in space satellites, which produce observations
without any (or limited) atmospheric aberrations. However, even though the
images from space are not affected by atmospheric seeing, the optical
properties of the instruments still limit the observations. In the case of
diffraction limited observations, the PSF establishes the maximum allowed
spatial resolution, defined as the distance between two nearby structures that
can be properly distinguished. In addition, the shape of the PSF induce a
dispersion of the light from different parts of the image, leading to what is
commonly termed as stray light or dispersed light. This effect produces that
light observed in a spatial location at the focal plane is a combination of the
light emitted in the object at relatively distant spatial locations. We aim to
correct the effect produced by the telescope's PSF using a deconvolution
method, and we decided to apply the code on Hinode/SP quiet Sun observations.
We analyze the validity of the deconvolution process with noisy data and we
infer the physical properties of quiet Sun magnetic elements after the
deconvolution process.Comment: 14 pages, 9 figure
HiRes deconvolution of Spitzer infrared images
Spitzer provides unprecedented sensitivity in the infrared (IR), but the spatial resolution is limited by a relatively small aperture (0.85 m) of the primary mirror. In order to maximize the scientific return it is desirable to use processing techniques which make the optimal use of the spatial information in the observations. We have developed a deconvolution technique for Spitzer images. The algorithm, "HiRes" and its implementation has been discussed by Backus et al. in 2005. Here we present examples of Spitzer IR images from the Infrared Array Camera (IRAC) and MIPS, reprocessed using this technique. Examples of HiRes processing include a variety of objects from point sources to complex extended regions. The examples include comparison of Spitzer deconvolved images with high-resolution Keck and Hubble Space Telescope images. HiRes deconvolution improves the visualization of spatial morphology by enhancing resolution (to sub-arcsecond levels in the IRAC bands) and removing the contaminating sidelobes from bright sources. The results thereby represent a significant improvement over previously-published Spitzer images. The benefits of HiRes include (a) sub-arcsec resolution (~0".6-0".8 for IRAC channels); (b) the ability to detect sources below the diffraction-limited confusion level; (c) the ability to separate blended sources, and thereby provide guidance to point-source extraction procedures; (d) an improved ability to show the spatial morphology of resolved sources. We suggest that it is a useful technique to identify features which are interesting enough for follow-up deeper analysis
UG^2: a Video Benchmark for Assessing the Impact of Image Restoration and Enhancement on Automatic Visual Recognition
Advances in image restoration and enhancement techniques have led to
discussion about how such algorithmscan be applied as a pre-processing step to
improve automatic visual recognition. In principle, techniques like deblurring
and super-resolution should yield improvements by de-emphasizing noise and
increasing signal in an input image. But the historically divergent goals of
the computational photography and visual recognition communities have created a
significant need for more work in this direction. To facilitate new research,
we introduce a new benchmark dataset called UG^2, which contains three
difficult real-world scenarios: uncontrolled videos taken by UAVs and manned
gliders, as well as controlled videos taken on the ground. Over 160,000
annotated frames forhundreds of ImageNet classes are available, which are used
for baseline experiments that assess the impact of known and unknown image
artifacts and other conditions on common deep learning-based object
classification approaches. Further, current image restoration and enhancement
techniques are evaluated by determining whether or not theyimprove baseline
classification performance. Results showthat there is plenty of room for
algorithmic innovation, making this dataset a useful tool going forward.Comment: Supplemental material: https://goo.gl/vVM1xe, Dataset:
https://goo.gl/AjA6En, CVPR 2018 Prize Challenge: ug2challenge.or
Recent Progress in Image Deblurring
This paper comprehensively reviews the recent development of image
deblurring, including non-blind/blind, spatially invariant/variant deblurring
techniques. Indeed, these techniques share the same objective of inferring a
latent sharp image from one or several corresponding blurry images, while the
blind deblurring techniques are also required to derive an accurate blur
kernel. Considering the critical role of image restoration in modern imaging
systems to provide high-quality images under complex environments such as
motion, undesirable lighting conditions, and imperfect system components, image
deblurring has attracted growing attention in recent years. From the viewpoint
of how to handle the ill-posedness which is a crucial issue in deblurring
tasks, existing methods can be grouped into five categories: Bayesian inference
framework, variational methods, sparse representation-based methods,
homography-based modeling, and region-based methods. In spite of achieving a
certain level of development, image deblurring, especially the blind case, is
limited in its success by complex application conditions which make the blur
kernel hard to obtain and be spatially variant. We provide a holistic
understanding and deep insight into image deblurring in this review. An
analysis of the empirical evidence for representative methods, practical
issues, as well as a discussion of promising future directions are also
presented.Comment: 53 pages, 17 figure
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