390 research outputs found
Curvelets and Ridgelets
International audienceDespite the fact that wavelets have had a wide impact in image processing, they fail to efficiently represent objects with highly anisotropic elements such as lines or curvilinear structures (e.g. edges). The reason is that wavelets are non-geometrical and do not exploit the regularity of the edge curve. The Ridgelet and the Curvelet [3, 4] transforms were developed as an answer to the weakness of the separable wavelet transform in sparsely representing what appears to be simple building atoms in an image, that is lines, curves and edges. Curvelets and ridgelets take the form of basis elements which exhibit high directional sensitivity and are highly anisotropic [5, 6, 7, 8]. These very recent geometric image representations are built upon ideas of multiscale analysis and geometry. They have had an important success in a wide range of image processing applications including denoising [8, 9, 10], deconvolution [11, 12], contrast enhancement [13], texture analysis [14, 15], detection [16], watermarking [17], component separation [18], inpainting [19, 20] or blind source separation[21, 22]. Curvelets have also proven useful in diverse fields beyond the traditional image processing application. Let’s cite for example seismic imaging [10, 23, 24], astronomical imaging [25, 26, 27], scientific computing and analysis of partial differential equations [28, 29]. Another reason for the success of ridgelets and curvelets is the availability of fast transform algorithms which are available in non-commercial software packages following the philosophy of reproducible research, see [30, 31]
Graph Signal Processing: Overview, Challenges and Applications
Research in Graph Signal Processing (GSP) aims to develop tools for
processing data defined on irregular graph domains. In this paper we first
provide an overview of core ideas in GSP and their connection to conventional
digital signal processing. We then summarize recent developments in developing
basic GSP tools, including methods for sampling, filtering or graph learning.
Next, we review progress in several application areas using GSP, including
processing and analysis of sensor network data, biological data, and
applications to image processing and machine learning. We finish by providing a
brief historical perspective to highlight how concepts recently developed in
GSP build on top of prior research in other areas.Comment: To appear, Proceedings of the IEE
A Panorama on Multiscale Geometric Representations, Intertwining Spatial, Directional and Frequency Selectivity
The richness of natural images makes the quest for optimal representations in
image processing and computer vision challenging. The latter observation has
not prevented the design of image representations, which trade off between
efficiency and complexity, while achieving accurate rendering of smooth regions
as well as reproducing faithful contours and textures. The most recent ones,
proposed in the past decade, share an hybrid heritage highlighting the
multiscale and oriented nature of edges and patterns in images. This paper
presents a panorama of the aforementioned literature on decompositions in
multiscale, multi-orientation bases or dictionaries. They typically exhibit
redundancy to improve sparsity in the transformed domain and sometimes its
invariance with respect to simple geometric deformations (translation,
rotation). Oriented multiscale dictionaries extend traditional wavelet
processing and may offer rotation invariance. Highly redundant dictionaries
require specific algorithms to simplify the search for an efficient (sparse)
representation. We also discuss the extension of multiscale geometric
decompositions to non-Euclidean domains such as the sphere or arbitrary meshed
surfaces. The etymology of panorama suggests an overview, based on a choice of
partially overlapping "pictures". We hope that this paper will contribute to
the appreciation and apprehension of a stream of current research directions in
image understanding.Comment: 65 pages, 33 figures, 303 reference
Phonetic Segmentation using a Wavelet-based Speech Cepstral Features and Sparse Representation Classifier, Journal of Telecommunications and Information Technology, 2021, nr 4
Speech segmentation is the process of dividing speech signal into distinct acoustic blocks that could be words, syllables or phonemes. Phonetic segmentation is about finding the exact boundaries for the different phonemes that composes a specific speech signal. This problem is crucial for many applications, i.e. automatic speech recognition (ASR). In this paper we propose a new model-based text independent phonetic segmentation method based on wavelet packet speech parametrization features and using the sparse representation classifier (SRC). Experiments were performed on two datasets, the first is an English one derived from TIMIT corpus, while the second is an Arabic one derived from the Arabic speech corpus. Results showed that the proposed wavelet packet de composition features outperform the MFCC features in speech segmentation task, in terms of both F1-score and R-measure on both datasets. Results also indicate that the SRC gives higher hit rate than the famous k-Nearest Neighbors (k-NN) classifier on TIMIT datase
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
Computer-Assisted Algorithms for Ultrasound Imaging Systems
Ultrasound imaging works on the principle of transmitting ultrasound waves into the body and
reconstructs the images of internal organs based on the strength of the echoes. Ultrasound imaging
is considered to be safer, economical and can image the organs in real-time, which makes it widely
used diagnostic imaging modality in health-care. Ultrasound imaging covers the broad spectrum
of medical diagnostics; these include diagnosis of kidney, liver, pancreas, fetal monitoring, etc.
Currently, the diagnosis through ultrasound scanning is clinic-centered, and the patients who are
in need of ultrasound scanning has to visit the hospitals for getting the diagnosis. The services of
an ultrasound system are constrained to hospitals and did not translate to its potential in remote
health-care and point-of-care diagnostics due to its high form factor, shortage of sonographers, low
signal to noise ratio, high diagnostic subjectivity, etc. In this thesis, we address these issues with an
objective of making ultrasound imaging more reliable to use in point-of-care and remote health-care
applications. To achieve the goal, we propose (i) computer-assisted algorithms to improve diagnostic
accuracy and assist semi-skilled persons in scanning, (ii) speckle suppression algorithms to improve
the diagnostic quality of ultrasound image, (iii) a reliable telesonography framework to address
the shortage of sonographers, and (iv) a programmable portable ultrasound scanner to operate in
point-of-care and remote health-care applications
- …