11,183 research outputs found
Semi-sparsity Priors for Image Structure Analysis and Extraction
Image structure-texture decomposition is a long-standing and fundamental
problem in both image processing and computer vision fields. In this paper, we
propose a generalized semi-sparse regularization framework for image structural
analysis and extraction, which allows us to decouple the underlying image
structures from complicated textural backgrounds. Combining with different
textural analysis models, such a regularization receives favorable properties
differing from many traditional methods. We demonstrate that it is not only
capable of preserving image structures without introducing notorious staircase
artifacts in polynomial-smoothing surfaces but is also applicable for
decomposing image textures with strong oscillatory patterns. Moreover, we also
introduce an efficient numerical solution based on an alternating direction
method of multipliers (ADMM) algorithm, which gives rise to a simple and
maneuverable way for image structure-texture decomposition. The versatility of
the proposed method is finally verified by a series of experimental results
with the capability of producing comparable or superior image decomposition
results against cutting-edge methods.Comment: 18 page
Graph Spectral Image Processing
Recent advent of graph signal processing (GSP) has spurred intensive studies
of signals that live naturally on irregular data kernels described by graphs
(e.g., social networks, wireless sensor networks). Though a digital image
contains pixels that reside on a regularly sampled 2D grid, if one can design
an appropriate underlying graph connecting pixels with weights that reflect the
image structure, then one can interpret the image (or image patch) as a signal
on a graph, and apply GSP tools for processing and analysis of the signal in
graph spectral domain. In this article, we overview recent graph spectral
techniques in GSP specifically for image / video processing. The topics covered
include image compression, image restoration, image filtering and image
segmentation
Detail-preserving and Content-aware Variational Multi-view Stereo Reconstruction
Accurate recovery of 3D geometrical surfaces from calibrated 2D multi-view
images is a fundamental yet active research area in computer vision. Despite
the steady progress in multi-view stereo reconstruction, most existing methods
are still limited in recovering fine-scale details and sharp features while
suppressing noises, and may fail in reconstructing regions with few textures.
To address these limitations, this paper presents a Detail-preserving and
Content-aware Variational (DCV) multi-view stereo method, which reconstructs
the 3D surface by alternating between reprojection error minimization and mesh
denoising. In reprojection error minimization, we propose a novel inter-image
similarity measure, which is effective to preserve fine-scale details of the
reconstructed surface and builds a connection between guided image filtering
and image registration. In mesh denoising, we propose a content-aware
-minimization algorithm by adaptively estimating the value and
regularization parameters based on the current input. It is much more promising
in suppressing noise while preserving sharp features than conventional
isotropic mesh smoothing. Experimental results on benchmark datasets
demonstrate that our DCV method is capable of recovering more surface details,
and obtains cleaner and more accurate reconstructions than state-of-the-art
methods. In particular, our method achieves the best results among all
published methods on the Middlebury dino ring and dino sparse ring datasets in
terms of both completeness and accuracy.Comment: 14 pages,16 figures. Submitted to IEEE Transaction on image
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Signal reconstruction via operator guiding
Signal reconstruction from a sample using an orthogonal projector onto a
guiding subspace is theoretically well justified, but may be difficult to
practically implement. We propose more general guiding operators, which
increase signal components in the guiding subspace relative to those in a
complementary subspace, e.g., iterative low-pass edge-preserving filters for
super-resolution of images. Two examples of super-resolution illustrate our
technology: a no-flash RGB photo guided using a high resolution flash RGB
photo, and a depth image guided using a high resolution RGB photo.Comment: 5 pages, 8 figures. To appear in Proceedings of SampTA 2017: Sampling
Theory and Applications, 12th International Conference, July 3-7, 2017,
Tallinn, Estoni
Directional Bilateral Filters
We propose a bilateral filter with a locally controlled domain kernel for
directional edge-preserving smoothing. Traditional bilateral filters use a
range kernel, which is responsible for edge preservation, and a fixed domain
kernel that performs smoothing. Our intuition is that orientation and
anisotropy of image structures should be incorporated into the domain kernel
while smoothing. For this purpose, we employ an oriented Gaussian domain kernel
locally controlled by a structure tensor. The oriented domain kernel combined
with a range kernel forms the directional bilateral filter. The two kernels
assist each other in effectively suppressing the influence of the outliers
while smoothing. To find the optimal parameters of the directional bilateral
filter, we propose the use of Stein's unbiased risk estimate (SURE). We test
the capabilities of the kernels separately as well as together, first on
synthetic images, and then on real endoscopic images. The directional bilateral
filter has better denoising performance than the Gaussian bilateral filter at
various noise levels in terms of peak signal-to-noise ratio (PSNR)
Finding Temporally Consistent Occlusion Boundaries in Videos using Geometric Context
We present an algorithm for finding temporally consistent occlusion
boundaries in videos to support segmentation of dynamic scenes. We learn
occlusion boundaries in a pairwise Markov random field (MRF) framework. We
first estimate the probability of an spatio-temporal edge being an occlusion
boundary by using appearance, flow, and geometric features. Next, we enforce
occlusion boundary continuity in a MRF model by learning pairwise occlusion
probabilities using a random forest. Then, we temporally smooth boundaries to
remove temporal inconsistencies in occlusion boundary estimation. Our proposed
framework provides an efficient approach for finding temporally consistent
occlusion boundaries in video by utilizing causality, redundancy in videos, and
semantic layout of the scene. We have developed a dataset with fully annotated
ground-truth occlusion boundaries of over 30 videos ($5000 frames). This
dataset is used to evaluate temporal occlusion boundaries and provides a much
needed baseline for future studies. We perform experiments to demonstrate the
role of scene layout, and temporal information for occlusion reasoning in
dynamic scenes.Comment: Applications of Computer Vision (WACV), 2015 IEEE Winter Conference
o
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