5,971 research outputs found
Unsupervised Sparse Dirichlet-Net for Hyperspectral Image Super-Resolution
In many computer vision applications, obtaining images of high resolution in
both the spatial and spectral domains are equally important. However, due to
hardware limitations, one can only expect to acquire images of high resolution
in either the spatial or spectral domains. This paper focuses on hyperspectral
image super-resolution (HSI-SR), where a hyperspectral image (HSI) with low
spatial resolution (LR) but high spectral resolution is fused with a
multispectral image (MSI) with high spatial resolution (HR) but low spectral
resolution to obtain HR HSI. Existing deep learning-based solutions are all
supervised that would need a large training set and the availability of HR HSI,
which is unrealistic. Here, we make the first attempt to solving the HSI-SR
problem using an unsupervised encoder-decoder architecture that carries the
following uniquenesses. First, it is composed of two encoder-decoder networks,
coupled through a shared decoder, in order to preserve the rich spectral
information from the HSI network. Second, the network encourages the
representations from both modalities to follow a sparse Dirichlet distribution
which naturally incorporates the two physical constraints of HSI and MSI.
Third, the angular difference between representations are minimized in order to
reduce the spectral distortion. We refer to the proposed architecture as
unsupervised Sparse Dirichlet-Net, or uSDN. Extensive experimental results
demonstrate the superior performance of uSDN as compared to the
state-of-the-art.Comment: Accepted by The IEEE Conference on Computer Vision and Pattern
Recognition (CVPR 2018, Spotlight
Sparse-to-Dense: Depth Prediction from Sparse Depth Samples and a Single Image
We consider the problem of dense depth prediction from a sparse set of depth
measurements and a single RGB image. Since depth estimation from monocular
images alone is inherently ambiguous and unreliable, to attain a higher level
of robustness and accuracy, we introduce additional sparse depth samples, which
are either acquired with a low-resolution depth sensor or computed via visual
Simultaneous Localization and Mapping (SLAM) algorithms. We propose the use of
a single deep regression network to learn directly from the RGB-D raw data, and
explore the impact of number of depth samples on prediction accuracy. Our
experiments show that, compared to using only RGB images, the addition of 100
spatially random depth samples reduces the prediction root-mean-square error by
50% on the NYU-Depth-v2 indoor dataset. It also boosts the percentage of
reliable prediction from 59% to 92% on the KITTI dataset. We demonstrate two
applications of the proposed algorithm: a plug-in module in SLAM to convert
sparse maps to dense maps, and super-resolution for LiDARs. Software and video
demonstration are publicly available.Comment: accepted to ICRA 2018. 8 pages, 8 figures, 3 tables. Video at
https://www.youtube.com/watch?v=vNIIT_M7x7Y. Code at
https://github.com/fangchangma/sparse-to-dens
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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