234 research outputs found
A Joint Intensity and Depth Co-Sparse Analysis Model for Depth Map Super-Resolution
High-resolution depth maps can be inferred from low-resolution depth
measurements and an additional high-resolution intensity image of the same
scene. To that end, we introduce a bimodal co-sparse analysis model, which is
able to capture the interdependency of registered intensity and depth
information. This model is based on the assumption that the co-supports of
corresponding bimodal image structures are aligned when computed by a suitable
pair of analysis operators. No analytic form of such operators exist and we
propose a method for learning them from a set of registered training signals.
This learning process is done offline and returns a bimodal analysis operator
that is universally applicable to natural scenes. We use this to exploit the
bimodal co-sparse analysis model as a prior for solving inverse problems, which
leads to an efficient algorithm for depth map super-resolution.Comment: 13 pages, 4 figure
Sparse Image Representation with Epitomes
Sparse coding, which is the decomposition of a vector using only a few basis
elements, is widely used in machine learning and image processing. The basis
set, also called dictionary, is learned to adapt to specific data. This
approach has proven to be very effective in many image processing tasks.
Traditionally, the dictionary is an unstructured "flat" set of atoms. In this
paper, we study structured dictionaries which are obtained from an epitome, or
a set of epitomes. The epitome is itself a small image, and the atoms are all
the patches of a chosen size inside this image. This considerably reduces the
number of parameters to learn and provides sparse image decompositions with
shiftinvariance properties. We propose a new formulation and an algorithm for
learning the structured dictionaries associated with epitomes, and illustrate
their use in image denoising tasks.Comment: Computer Vision and Pattern Recognition, Colorado Springs : United
States (2011
Sparse Correlation Kernel Analysis and Reconstruction
This paper presents a new paradigm for signal reconstruction and superresolution, Correlation Kernel Analysis (CKA), that is based on the selection of a sparse set of bases from a large dictionary of class- specific basis functions. The basis functions that we use are the correlation functions of the class of signals we are analyzing. To choose the appropriate features from this large dictionary, we use Support Vector Machine (SVM) regression and compare this to traditional Principal Component Analysis (PCA) for the tasks of signal reconstruction, superresolution, and compression. The testbed we use in this paper is a set of images of pedestrians. This paper also presents results of experiments in which we use a dictionary of multiscale basis functions and then use Basis Pursuit De-Noising to obtain a sparse, multiscale approximation of a signal. The results are analyzed and we conclude that 1) when used with a sparse representation technique, the correlation function is an effective kernel for image reconstruction and superresolution, 2) for image compression, PCA and SVM have different tradeoffs, depending on the particular metric that is used to evaluate the results, 3) in sparse representation techniques, L_1 is not a good proxy for the true measure of sparsity, L_0, and 4) the L_epsilon norm may be a better error metric for image reconstruction and compression than the L_2 norm, though the exact psychophysical metric should take into account high order structure in images
Spectral Superresolution of Multispectral Imagery with Joint Sparse and Low-Rank Learning
Extensive attention has been widely paid to enhance the spatial resolution of
hyperspectral (HS) images with the aid of multispectral (MS) images in remote
sensing. However, the ability in the fusion of HS and MS images remains to be
improved, particularly in large-scale scenes, due to the limited acquisition of
HS images. Alternatively, we super-resolve MS images in the spectral domain by
the means of partially overlapped HS images, yielding a novel and promising
topic: spectral superresolution (SSR) of MS imagery. This is challenging and
less investigated task due to its high ill-posedness in inverse imaging. To
this end, we develop a simple but effective method, called joint sparse and
low-rank learning (J-SLoL), to spectrally enhance MS images by jointly learning
low-rank HS-MS dictionary pairs from overlapped regions. J-SLoL infers and
recovers the unknown hyperspectral signals over a larger coverage by sparse
coding on the learned dictionary pair. Furthermore, we validate the SSR
performance on three HS-MS datasets (two for classification and one for
unmixing) in terms of reconstruction, classification, and unmixing by comparing
with several existing state-of-the-art baselines, showing the effectiveness and
superiority of the proposed J-SLoL algorithm. Furthermore, the codes and
datasets will be available at:
https://github.com/danfenghong/IEEE\_TGRS\_J-SLoL, contributing to the RS
community
An Efficient Image Enlargement Method for Image Sensors of Mobile in Embedded Systems
Main challenges for image enlargement methods in embedded systems come from the requirements of good performance, low computational cost, and low memory usage. This paper proposes an efficient image enlargement method which can meet these requirements in embedded system. Firstly, to improve the performance of enlargement methods, this method extracts different kind of features for different morphologies with different approaches. Then, various dictionaries based on different kind of features are learned, which represent the image in a more efficient manner. Secondly, to accelerate the enlargement speed and reduce the memory usage, this method divides the atoms of each dictionary into several clusters. For each cluster, separate projection matrix is calculated. This method reformulates the problem as a least squares regression. The high-resolution (HR) images can be reconstructed based on a few projection matrixes. Numerous experiment results show that this method has advantages such as being efficient and real-time and having less memory cost. These advantages make this method easy to implement in mobile embedded system
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