294 research outputs found
Multi-modal Image Processing based on Coupled Dictionary Learning
In real-world scenarios, many data processing problems often involve
heterogeneous images associated with different imaging modalities. Since these
multimodal images originate from the same phenomenon, it is realistic to assume
that they share common attributes or characteristics. In this paper, we propose
a multi-modal image processing framework based on coupled dictionary learning
to capture similarities and disparities between different image modalities. In
particular, our framework can capture favorable structure similarities across
different image modalities such as edges, corners, and other elementary
primitives in a learned sparse transform domain, instead of the original pixel
domain, that can be used to improve a number of image processing tasks such as
denoising, inpainting, or super-resolution. Practical experiments demonstrate
that incorporating multimodal information using our framework brings notable
benefits.Comment: SPAWC 2018, 19th IEEE International Workshop On Signal Processing
Advances In Wireless Communication
Fast Color-guided Depth Denoising for RGB-D Images by Graph Filtering
Depth images captured by off-the-shelf RGB-D cameras suffer from much
stronger noise than color images. In this paper, we propose a method to denoise
the depth images in RGB-D images by color-guided graph filtering. Our iterative
method contains two components: color-guided similarity graph construction, and
graph filtering on the depth signal. Implemented in graph vertex domain,
filtering is accelerated as computation only occurs among neighboring vertices.
Experimental results show that our method outperforms state-of-art depth image
denoising methods significantly both on quality and efficiency.Comment: 5 pages, 4 figure
2D Phase Unwrapping via Graph Cuts
Phase imaging technologies such as interferometric synthetic aperture radar (InSAR),
magnetic resonance imaging (MRI), or optical interferometry, are nowadays widespread
and with an increasing usage. The so-called phase unwrapping, which consists in the in-
ference of the absolute phase from the modulo-2Ï€ phase, is a critical step in many of their
processing chains, yet still one of its most challenging problems. We introduce an en-
ergy minimization based approach to 2D phase unwrapping. In this approach we address
the problem by adopting a Bayesian point of view and a Markov random field (MRF)
to model the phase. The maximum a posteriori estimation of the absolute phase gives
rise to an integer optimization problem, for which we introduce a family of efficient algo-
rithms based on existing graph cuts techniques. We term our approach and algorithms
PUMA, for Phase Unwrapping MAx flow. As long as the prior potential of the MRF
is convex, PUMA guarantees an exact global solution. In particular it solves exactly all
the minimum L p norm (p ≥ 1) phase unwrapping problems, unifying in that sense, a set
of existing independent algorithms. For non convex potentials we introduce a version of
PUMA that, while yielding only approximate solutions, gives very useful phase unwrap-
ping results. The main characteristic of the introduced solutions is the ability to blindly
preserve discontinuities. Extending the previous versions of PUMA, we tackle denoising by
exploiting a multi-precision idea, which allows us to use the same rationale both for phase
unwrapping and denoising. Finally, the last presented version of PUMA uses a frequency
diversity concept to unwrap phase images having large phase rates. A representative set
of experiences illustrates the performance of PUMA
Multimodal Image Denoising based on Coupled Dictionary Learning
In this paper, we propose a new multimodal image denoising approach to
attenuate white Gaussian additive noise in a given image modality under the aid
of a guidance image modality. The proposed coupled image denoising approach
consists of two stages: coupled sparse coding and reconstruction. The first
stage performs joint sparse transform for multimodal images with respect to a
group of learned coupled dictionaries, followed by a shrinkage operation on the
sparse representations. Then, in the second stage, the shrunken
representations, together with coupled dictionaries, contribute to the
reconstruction of the denoised image via an inverse transform. The proposed
denoising scheme demonstrates the capability to capture both the common and
distinct features of different data modalities. This capability makes our
approach more robust to inconsistencies between the guidance and the target
images, thereby overcoming drawbacks such as the texture copying artifacts.
Experiments on real multimodal images demonstrate that the proposed approach is
able to better employ guidance information to bring notable benefits in the
image denoising task with respect to the state-of-the-art.Comment: 2018 IEEE International Conference on Image Processing (ICIP). arXiv
admin note: text overlap with arXiv:1806.0988
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