91 research outputs found
Locally-adapted convolution-based super-resolution of irregularly-sampled ocean remote sensing data
Super-resolution is a classical problem in image processing, with numerous
applications to remote sensing image enhancement. Here, we address the
super-resolution of irregularly-sampled remote sensing images. Using an optimal
interpolation as the low-resolution reconstruction, we explore locally-adapted
multimodal convolutional models and investigate different dictionary-based
decompositions, namely based on principal component analysis (PCA), sparse
priors and non-negativity constraints. We consider an application to the
reconstruction of sea surface height (SSH) fields from two information sources,
along-track altimeter data and sea surface temperature (SST) data. The reported
experiments demonstrate the relevance of the proposed model, especially
locally-adapted parametrizations with non-negativity constraints, to outperform
optimally-interpolated reconstructions.Comment: 4 pages, 3 figure
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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