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Ultrasound Image Filtering and Reconstruction Using DCT/IDCT Filter Structure
In this paper, a new recursive structure based on the convolution model of discrete cosine transform (DCT) for designing of a finite impulse response (FIR) digital filter is proposed. In our derivation, we start with the convolution model of DCT-II to use its Z-transform for the proposed filter structure perspective. Moreover, using the same algorithm, a filter base implementation of the inverse DCT (IDCT) for image reconstruction is developed. The computational time experiments of the proposed DCT/IDCT filter(s) demonstrate that the proposed filters achieve faster elapsed CPU time compared to the direct recursive structures and recursive algorithms for the DCT/IDCT with Arbitrary Length. Experimental results on clinical ultrasound images and comparisons with classical Wiener filter, non-local mean (NLM) filter and total variation (TV) algorithms are used to validate the improvements of the proposed approaches in both noise reduction and reconstruction performance for ultrasound images
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
Accelerated High-Resolution Photoacoustic Tomography via Compressed Sensing
Current 3D photoacoustic tomography (PAT) systems offer either high image
quality or high frame rates but are not able to deliver high spatial and
temporal resolution simultaneously, which limits their ability to image dynamic
processes in living tissue. A particular example is the planar Fabry-Perot (FP)
scanner, which yields high-resolution images but takes several minutes to
sequentially map the photoacoustic field on the sensor plane, point-by-point.
However, as the spatio-temporal complexity of many absorbing tissue structures
is rather low, the data recorded in such a conventional, regularly sampled
fashion is often highly redundant. We demonstrate that combining variational
image reconstruction methods using spatial sparsity constraints with the
development of novel PAT acquisition systems capable of sub-sampling the
acoustic wave field can dramatically increase the acquisition speed while
maintaining a good spatial resolution: First, we describe and model two general
spatial sub-sampling schemes. Then, we discuss how to implement them using the
FP scanner and demonstrate the potential of these novel compressed sensing PAT
devices through simulated data from a realistic numerical phantom and through
measured data from a dynamic experimental phantom as well as from in-vivo
experiments. Our results show that images with good spatial resolution and
contrast can be obtained from highly sub-sampled PAT data if variational image
reconstruction methods that describe the tissues structures with suitable
sparsity-constraints are used. In particular, we examine the use of total
variation regularization enhanced by Bregman iterations. These novel
reconstruction strategies offer new opportunities to dramatically increase the
acquisition speed of PAT scanners that employ point-by-point sequential
scanning as well as reducing the channel count of parallelized schemes that use
detector arrays.Comment: submitted to "Physics in Medicine and Biology
Quantum median filter for total variation image denoising
In this new computing paradigm, named quantum computing, researchers from all over
the world are taking their first steps in designing quantum circuits for image process-
ing, through a difficult process of knowledge transfer. This effort is named quantum
image processing, an emerging research field pushed by powerful parallel comput-
ing capabilities of quantum computers. This work goes in this direction and proposes
the challenging development of a powerful method of image denoising, such as the
total variation (TV) model, in a quantum environment. The proposed quantum TV is
described and its sub-components are analysed. Despite the natural limitations of the
current capabilities of quantum devices, the experimental results show a competitive
denoising performance compared to the classical variational TV counterpar
Multiscale bilateral filtering for improving image quality in digital breast tomosynthesis
Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/135115/1/mp3283.pd
Graph Signal Restoration Using Nested Deep Algorithm Unrolling
Graph signal processing is a ubiquitous task in many applications such as
sensor, social, transportation and brain networks, point cloud processing, and
graph neural networks. Graph signals are often corrupted through sensing
processes, and need to be restored for the above applications. In this paper,
we propose two graph signal restoration methods based on deep algorithm
unrolling (DAU). First, we present a graph signal denoiser by unrolling
iterations of the alternating direction method of multiplier (ADMM). We then
propose a general restoration method for linear degradation by unrolling
iterations of Plug-and-Play ADMM (PnP-ADMM). In the second method, the unrolled
ADMM-based denoiser is incorporated as a submodule. Therefore, our restoration
method has a nested DAU structure. Thanks to DAU, parameters in the proposed
denoising/restoration methods are trainable in an end-to-end manner. Since the
proposed restoration methods are based on iterations of a (convex) optimization
algorithm, the method is interpretable and keeps the number of parameters small
because we only need to tune graph-independent regularization parameters. We
solve two main problems in existing graph signal restoration methods: 1)
limited performance of convex optimization algorithms due to fixed parameters
which are often determined manually. 2) large number of parameters of graph
neural networks that result in difficulty of training. Several experiments for
graph signal denoising and interpolation are performed on synthetic and
real-world data. The proposed methods show performance improvements to several
existing methods in terms of root mean squared error in both tasks
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