1,474 research outputs found
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
Depth Superresolution using Motion Adaptive Regularization
Spatial resolution of depth sensors is often significantly lower compared to
that of conventional optical cameras. Recent work has explored the idea of
improving the resolution of depth using higher resolution intensity as a side
information. In this paper, we demonstrate that further incorporating temporal
information in videos can significantly improve the results. In particular, we
propose a novel approach that improves depth resolution, exploiting the
space-time redundancy in the depth and intensity using motion-adaptive low-rank
regularization. Experiments confirm that the proposed approach substantially
improves the quality of the estimated high-resolution depth. Our approach can
be a first component in systems using vision techniques that rely on high
resolution depth information
Convolutional Dictionary Regularizers for Tomographic Inversion
There has been a growing interest in the use of data-driven regularizers to
solve inverse problems associated with computational imaging systems. The
convolutional sparse representation model has recently gained attention, driven
by the development of fast algorithms for solving the dictionary learning and
sparse coding problems for sufficiently large images and data sets.
Nevertheless, this model has seen very limited application to tomographic
reconstruction problems. In this paper, we present a model-based tomographic
reconstruction algorithm using a learnt convolutional dictionary as a
regularizer. The key contribution is the use of a data-dependent weighting
scheme for the l1 regularization to construct an effective denoising method
that is integrated into the inversion using the Plug-and-Play reconstruction
framework. Using simulated data sets we demonstrate that our approach can
improve performance over traditional regularizers based on a Markov random
field model and a patch-based sparse representation model for sparse and
limited-view tomographic data sets
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