35 research outputs found
Proceedings of the second "international Traveling Workshop on Interactions between Sparse models and Technology" (iTWIST'14)
The implicit objective of the biennial "international - Traveling Workshop on
Interactions between Sparse models and Technology" (iTWIST) is to foster
collaboration between international scientific teams by disseminating ideas
through both specific oral/poster presentations and free discussions. For its
second edition, the iTWIST workshop took place in the medieval and picturesque
town of Namur in Belgium, from Wednesday August 27th till Friday August 29th,
2014. The workshop was conveniently located in "The Arsenal" building within
walking distance of both hotels and town center. iTWIST'14 has gathered about
70 international participants and has featured 9 invited talks, 10 oral
presentations, and 14 posters on the following themes, all related to the
theory, application and generalization of the "sparsity paradigm":
Sparsity-driven data sensing and processing; Union of low dimensional
subspaces; Beyond linear and convex inverse problem; Matrix/manifold/graph
sensing/processing; Blind inverse problems and dictionary learning; Sparsity
and computational neuroscience; Information theory, geometry and randomness;
Complexity/accuracy tradeoffs in numerical methods; Sparsity? What's next?;
Sparse machine learning and inference.Comment: 69 pages, 24 extended abstracts, iTWIST'14 website:
http://sites.google.com/site/itwist1
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
Integrated cosparse analysis model with explicit edge inconsistency measurement for guided depth map upsampling
© 2018 SPIE and IS & T. A low-resolution depth map can be upsampled through the guidance from the registered high-resolution color image. This type of method is so-called guided depth map upsampling. Among the existing methods based on Markov random field (MRF), either data-driven or model-based prior is adopted to construct the regularization term. The data-driven prior can implicitly reveal the relation between color-depth image pair by training on external data. The model-based prior provides the anisotropic smoothness constraint guided by high-resolution color image. These types of priors can complement each other to solve the ambiguity in guided depth map upsampling. An MRF-based approach is proposed that takes both of them into account to regularize the depth map. Based on analysis sparse coding, the data-driven prior is defined by joint cosparsity on the vectors transformed from color-depth patches using the pair of learned operators. It is based on the assumption that the cosupports of such bimodal image structures computed by the operators are aligned. The edge inconsistency measurement is explicitly calculated, which is embedded into the model-based prior. It can significantly mitigate texture-copying artifacts. The experimental results on Middlebury datasets demonstrate the validity of the proposed method that outperforms seven state-of-the-art approaches
Infrared and Visible Image Fusion using a Deep Learning Framework
In recent years, deep learning has become a very active research tool which
is used in many image processing fields. In this paper, we propose an effective
image fusion method using a deep learning framework to generate a single image
which contains all the features from infrared and visible images. First, the
source images are decomposed into base parts and detail content. Then the base
parts are fused by weighted-averaging. For the detail content, we use a deep
learning network to extract multi-layer features. Using these features, we use
l_1-norm and weighted-average strategy to generate several candidates of the
fused detail content. Once we get these candidates, the max selection strategy
is used to get final fused detail content. Finally, the fused image will be
reconstructed by combining the fused base part and detail content. The
experimental results demonstrate that our proposed method achieves
state-of-the-art performance in both objective assessment and visual quality.
The Code of our fusion method is available at
https://github.com/hli1221/imagefusion_deeplearningComment: 6 pages, 6 figures, 2 tables, ICPR 2018(accepted
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
MDLatLRR: A novel decomposition method for infrared and visible image fusion
Image decomposition is crucial for many image processing tasks, as it allows
to extract salient features from source images. A good image decomposition
method could lead to a better performance, especially in image fusion tasks. We
propose a multi-level image decomposition method based on latent low-rank
representation(LatLRR), which is called MDLatLRR. This decomposition method is
applicable to many image processing fields. In this paper, we focus on the
image fusion task. We develop a novel image fusion framework based on MDLatLRR,
which is used to decompose source images into detail parts(salient features)
and base parts. A nuclear-norm based fusion strategy is used to fuse the detail
parts, and the base parts are fused by an averaging strategy. Compared with
other state-of-the-art fusion methods, the proposed algorithm exhibits better
fusion performance in both subjective and objective evaluation.Comment: IEEE Trans. Image Processing 2020, 14 pages, 17 figures, 3 table