493 research outputs found
RIBBONS: Rapid Inpainting Based on Browsing of Neighborhood Statistics
Image inpainting refers to filling missing places in images using neighboring
pixels. It also has many applications in different tasks of image processing.
Most of these applications enhance the image quality by significant unwanted
changes or even elimination of some existing pixels. These changes require
considerable computational complexities which in turn results in remarkable
processing time. In this paper we propose a fast inpainting algorithm called
RIBBONS based on selection of patches around each missing pixel. This would
accelerate the execution speed and the capability of online frame inpainting in
video. The applied cost-function is a combination of statistical and spatial
features in all neighboring pixels. We evaluate some candidate patches using
the proposed cost function and minimize it to achieve the final patch.
Experimental results show the higher speed of 'Ribbons' in comparison with
previous methods while being comparable in terms of PSNR and SSIM for the
images in MISC dataset
A Second Order TV-type Approach for Inpainting and Denoising Higher Dimensional Combined Cyclic and Vector Space Data
In this paper we consider denoising and inpainting problems for higher
dimensional combined cyclic and linear space valued data. These kind of data
appear when dealing with nonlinear color spaces such as HSV, and they can be
obtained by changing the space domain of, e.g., an optical flow field to polar
coordinates. For such nonlinear data spaces, we develop algorithms for the
solution of the corresponding second order total variation (TV) type problems
for denoising, inpainting as well as the combination of both. We provide a
convergence analysis and we apply the algorithms to concrete problems.Comment: revised submitted versio
Sparse Modeling for Image and Vision Processing
In recent years, a large amount of multi-disciplinary research has been
conducted on sparse models and their applications. In statistics and machine
learning, the sparsity principle is used to perform model selection---that is,
automatically selecting a simple model among a large collection of them. In
signal processing, sparse coding consists of representing data with linear
combinations of a few dictionary elements. Subsequently, the corresponding
tools have been widely adopted by several scientific communities such as
neuroscience, bioinformatics, or computer vision. The goal of this monograph is
to offer a self-contained view of sparse modeling for visual recognition and
image processing. More specifically, we focus on applications where the
dictionary is learned and adapted to data, yielding a compact representation
that has been successful in various contexts.Comment: 205 pages, to appear in Foundations and Trends in Computer Graphics
and Visio
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