100,340 research outputs found
EDGE PRESERVING FILTERS USING GEODESIC DISTANCES ON WEIGHTED ORTHOGONAL DOMAINS
We introduce a framework for image enhancement, which smooths images while preserving edge information. Domain (spatial) and range (feature) information are combined in one single measure in a principled way. This measure turns out to be the geodesic distance between pixels, calculated on weighted orthogonal domains. The weight function is computed to capture the underlying structure of the image manifold, but allowing at the same time to efficiently solve, using the Fast Marching algorithm on orthogonal domains, the eikonal equation to obtain the geodesic distances. We show promising results in edge-preserving denoising of gray scale, color and texture images. Index Terms — Adaptive smoothing filters, geodesic distance, Fast Marching Method, edge-preserving filtering. 1
Deep Bilateral Learning for Real-Time Image Enhancement
Performance is a critical challenge in mobile image processing. Given a
reference imaging pipeline, or even human-adjusted pairs of images, we seek to
reproduce the enhancements and enable real-time evaluation. For this, we
introduce a new neural network architecture inspired by bilateral grid
processing and local affine color transforms. Using pairs of input/output
images, we train a convolutional neural network to predict the coefficients of
a locally-affine model in bilateral space. Our architecture learns to make
local, global, and content-dependent decisions to approximate the desired image
transformation. At runtime, the neural network consumes a low-resolution
version of the input image, produces a set of affine transformations in
bilateral space, upsamples those transformations in an edge-preserving fashion
using a new slicing node, and then applies those upsampled transformations to
the full-resolution image. Our algorithm processes high-resolution images on a
smartphone in milliseconds, provides a real-time viewfinder at 1080p
resolution, and matches the quality of state-of-the-art approximation
techniques on a large class of image operators. Unlike previous work, our model
is trained off-line from data and therefore does not require access to the
original operator at runtime. This allows our model to learn complex,
scene-dependent transformations for which no reference implementation is
available, such as the photographic edits of a human retoucher.Comment: 12 pages, 14 figures, Siggraph 201
Detection of leaf structures in close-range hyperspectral images using morphological fusion
Close-range hyperspectral images are a promising source of information in plant biology, in particular, for in vivo study of physiological changes. In this study, we investigate how data fusion can improve the detection of leaf elements by combining pixel reflectance and morphological information. The detection of image regions associated to the leaf structures is the first step toward quantitative analysis on the physical effects that genetic manipulation, disease infections, and environmental conditions have in plants. We tested our fusion approach on Musa acuminata (banana) leaf images and compared its discriminant capability to similar techniques used in remote sensing. Experimental results demonstrate the efficiency of our fusion approach, with significant improvements over some conventional methods
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
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