29,831 research outputs found
Simulated Annealing for JPEG Quantization
JPEG is one of the most widely used image formats, but in some ways remains
surprisingly unoptimized, perhaps because some natural optimizations would go
outside the standard that defines JPEG. We show how to improve JPEG compression
in a standard-compliant, backward-compatible manner, by finding improved
default quantization tables. We describe a simulated annealing technique that
has allowed us to find several quantization tables that perform better than the
industry standard, in terms of both compressed size and image fidelity.
Specifically, we derive tables that reduce the FSIM error by over 10% while
improving compression by over 20% at quality level 95 in our tests; we also
provide similar results for other quality levels. While we acknowledge our
approach can in some images lead to visible artifacts under large
magnification, we believe use of these quantization tables, or additional
tables that could be found using our methodology, would significantly reduce
JPEG file sizes with improved overall image quality.Comment: Appendix not included in arXiv version due to size restrictions. For
full paper go to:
http://www.eecs.harvard.edu/~michaelm/SimAnneal/PAPER/simulated-annealing-jpeg.pd
Optimized imaging using non-rigid registration
The extraordinary improvements of modern imaging devices offer access to data
with unprecedented information content. However, widely used image processing
methodologies fall far short of exploiting the full breadth of information
offered by numerous types of scanning probe, optical, and electron
microscopies. In many applications, it is necessary to keep measurement
intensities below a desired threshold. We propose a methodology for extracting
an increased level of information by processing a series of data sets
suffering, in particular, from high degree of spatial uncertainty caused by
complex multiscale motion during the acquisition process. An important role is
played by a nonrigid pixel-wise registration method that can cope with low
signal-to-noise ratios. This is accompanied by formulating objective quality
measures which replace human intervention and visual inspection in the
processing chain. Scanning transmission electron microscopy of siliceous
zeolite material exhibits the above-mentioned obstructions and therefore serves
as orientation and a test of our procedures
Image enhancement using fuzzy intensity measure and adaptive clipping histogram equalization
Image enhancement aims at processing an input
image so that the visual content of the output image is more
pleasing or more useful for certain applications. Although
histogram equalization is widely used in image enhancement due
to its simplicity and effectiveness, it changes the mean brightness
of the enhanced image and introduces a high level of noise and
distortion. To address these problems, this paper proposes
image enhancement using fuzzy intensity measure and adaptive
clipping histogram equalization (FIMHE). FIMHE uses fuzzy
intensity measure to first segment the histogram of the original
image, and then clip the histogram adaptively in order to
prevent excessive image enhancement. Experiments on the
Berkeley database and CVF-UGR-Image database show that
FIMHE outperforms state-of-the-art histogram equalization
based methods
Bulge plus disc and S\'ersic decomposition catalogues for 16,908 galaxies in the SDSS Stripe 82 co-adds: A detailed study of the structural measurements
Quantitative characterization of galaxy morphology is vital in enabling
comparison of observations to predictions from galaxy formation theory.
However, without significant overlap between the observational footprints of
deep and shallow galaxy surveys, the extent to which structural measurements
for large galaxy samples are robust to image quality (e.g., depth, spatial
resolution) cannot be established. Deep images from the Sloan Digital Sky
Survey (SDSS) Stripe 82 co-adds provide a unique solution to this problem -
offering magnitudes improvement in depth with respect to SDSS Legacy
images. Having similar spatial resolution to Legacy, the co-adds make it
possible to examine the sensitivity of parametric morphologies to depth alone.
Using the Gim2D surface-brightness decomposition software, we provide public
morphology catalogs for 16,908 galaxies in the Stripe 82 co-adds. Our
methods and selection are completely consistent with the Simard et al. (2011)
and Mendel et al. (2014) photometric decompositions. We rigorously compare
measurements in the deep and shallow images. We find no systematics in total
magnitudes and sizes except for faint galaxies in the -band and the
brightest galaxies in each band. However, characterization of bulge-to-total
fractions is significantly improved in the deep images. Furthermore, statistics
used to determine whether single-S\'ersic or two-component (e.g., bulge+disc)
models are required become more bimodal in the deep images. Lastly, we show
that asymmetries are enhanced in the deep images and that the enhancement is
positively correlated with the asymmetries measured in Legacy images.Comment: 27 pages, 14 figures. MNRAS accepted. Our catalogs are available in
TXT and SQL formats at
http://orca.phys.uvic.ca/~cbottrel/share/Stripe82/Catalogs
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