http://ieeexplore.ieee.org/iel5/5/31047/01444179.pdf

Fundamentals of acoustics

Quantum Computing (QC) promises to elevate computing speed by an estimated 100 million times. Several applications, including signal processing, machine learning, big data, communication, and cryptography, will benefit from quantum computing. This paper provides a brief survey of quantum information processing algorithms with an emphasis on machine learning. We begin first, covering with an introduction to quantum systems. Then we describe briefly the fundamental blocks and principles of quantum mechanics, and we present several related QC concepts such as qubits, correlation, and entanglement. We also present simulations and tools for the quantum implementation of select algorithms. We cover specifically Quantum Machine Learning (QML) and demonstrate simple implementations. The paper also describes current research and provides an extensive bibliography for further reading.

Quantum Information Processing Algorithms with Emphasis on Machine Learning

While restoration methods have been extensively studied in ultrasound (US) imaging, only few recent works have focused on modeling and understanding the blur from a physical point of view even in simple configurations, such as lossless homogeneous media. Despite a highly nonstationary blur due to diffraction effects, many techniques rely on simplistic approximations based on shift-invariant models or sectional methods and their efficiency has not been demonstrated for plane-wave (PW) and diverging-wave (DW) imaging. In this paper, we first propose a physical model of nonstationary blur in the context of PW and DW imaging. The blur operation is expressed as a composition of a U.S. propagation operator and a delay-and-sum operator, each of which has derived in previous works. We show that such a composition leads to a standard model of nonstationary blur as a Fredholm integral of the first kind. Second, we describe an approximation of the blur in the discrete domain based on the above decomposition coupled with an appropriate discretization of the latent element-raw-data space. We show theoretically and empirically that its evaluation, using such an approximation, scales linearly instead of quadratically with respect to the grid size, better than shift-invariant approaches. Through simulations and in vivo experimental data, we demonstrate that using the proposed model in the context of maximum-a-posteriori image restoration results in a higher image quality than using state-of-the-art shift-invariant models.

/pdf/a-physical-model-of-nonstationary-blur-in-ultrasound-imaging-49nkp7ppz6.pdf

A Physical Model of Nonstationary Blur in Ultrasound Imaging

Scanning acoustic microscopy (SAM) or Acoustic Micro Imaging (AMI) is a powerful, non-destructive technique that can detect hidden defects in elastic and biological samples as well as non-transparent hard materials. By monitoring the internal features of a sample in three-dimensional integration, this technique can efficiently find physical defects such as cracks, voids, and delamination with high sensitivity. In recent years, advanced techniques such as ultrasound impedance microscopy, ultrasound speed microscopy, and scanning acoustic gigahertz microscopy have been developed for applications in industries and in the medical field to provide additional information on the internal stress, viscoelastic, and anisotropic, or nonlinear properties. X-ray, magnetic resonance, and infrared techniques are the other competitive and widely used methods. However, they have their own advantages and limitations owing to their inherent properties such as different light sources and sensors. This paper provides an overview of the principle of SAM and presents a few results to demonstrate the applications of modern acoustic imaging technology. A variety of inspection modes, such as vertical, horizontal, and diagonal cross-sections have been presented by employing the focus pathway and image reconstruction algorithm. Images have been reconstructed from the reflected echoes resulting from the change in the acoustic impedance at the interface of the material layers or defects. The results described in this paper indicate that the novel acoustic technology can expand the scope of SAM as a versatile diagnostic tool requiring less time and having a high efficiency.

/pdf/scanning-acoustic-microscopy-for-material-evaluation-1ecxdjgvnj.pdf

Scanning acoustic microscopy for material evaluation

A new Plug-and-Play (PnP) alternating direction of multipliers (ADMM) scheme is proposed in this paper, by embedding a recently introduced adaptive denoiser using the Schroedinger equation’s solutions of quantum physics. The potential of the proposed model is studied for Poisson image deconvolution, which is a common problem occurring in number of imaging applications, such as limited photon acquisition or X-ray computed tomography. Numerical results show the efficiency and good adaptability of the proposed scheme compared to recent state-of-the-art techniques, for both high and low signal-to-noise ratio scenarios. This performance gain regardless of the amount of noise affecting the observations is explained by the flexibility of the embedded quantum denoiser constructed without anticipating any prior statistics about the noise, which is one of the main advantages of this method. The main novelty of this work resided in the integration of a modified quantum denoiser into the PnP-ADMM framework and the numerical proof of convergence of the resulting algorithm.

Plug-and-Play Quantum Adaptive Denoiser for Deconvolving Poisson Noisy Images

This paper studies the detection of anomalous crop development at the parcel-level based on an unsupervised outlier detection technique. The experimental validation is conducted on rapeseed and wheat parcels located in Beauce (France). The proposed methodology consists of four sequential steps: (1) preprocessing of synthetic aperture radar (SAR) and multispectral images acquired using Sentinel-1 and Sentinel-2 satellites, (2) extraction of SAR and multispectral pixel-level features, (3) computation of parcel-level features using zonal statistics and (4) outlier detection. The different types of anomalies that can affect the studied crops are analyzed and described. The different factors that can influence the outlier detection results are investigated with a particular attention devoted to the synergy between Sentinel-1 and Sentinel-2 data. Overall, the best performance is obtained when using jointly a selection of Sentinel-1 and Sentinel-2 features with the isolation forest algorithm. The selected features are co-polarized (VV) and cross-polarized (VH) backscattering coefficients for Sentinel-1 and five Vegetation Indexes for Sentinel-2 (among us, the Normalized Difference Vegetation Index and two variants of the Normalized Difference Water). When using these features with an outlier ratio of 10%, the percentage of detected true positives (i.e., crop anomalies) is equal to 94.1% for rapeseed parcels and 95.5% for wheat parcels.

Outlier detection at the parcel-level in wheat and rapeseed crops using multispectral and SAR time series

Decomposition of digital signals and images into other basis or dictionaries than time or space domains is a very common approach in signal and image processing and analysis. Such a decomposition is commonly obtained using fixed transforms (e.g., Fourier or wavelet) or dictionaries learned from example databases or from the signal or image itself. In this work, we investigate in detail a new approach of constructing such a signal or image-dependent bases inspired by quantum mechanics tools, i.e., by considering the signal or image as a potential in the discretized Schroedinger equation. To illustrate the potential of the proposed decomposition, denoising results are reported in the case of Gaussian, Poisson, and speckle noise and compared to the state of the art algorithms based on wavelet shrinkage, total variation regularization or patch-wise sparse coding in learned dictionaries, non-local means image denoising, and graph signal processing.

/pdf/quantum-mechanics-based-signal-and-image-representation-3vbe7s8mai.pdf

Quantum Mechanics-Based Signal and Image Representation: Application to Denoising

Decomposing an image through Fourier, DCT or wavelet transforms is still a common approach in digital image processing, in number of applications such as denoising. In this context, data-driven dictionaries and in particular exploiting the redundancy withing patches extracted from one or several images allowed important improvements. This paper proposes an original idea of constructing such an image-dependent basis inspired by the principles of quantum many-body physics. The similarity between two image patches is introduced in the formalism through a term akin to interaction terms in quantum mechanics. The main contribution of the paper is thus to introduce this original way of exploiting quantum many-body ideas in image processing, which opens interesting perspectives in image denoising. The potential of the proposed adaptive decomposition is illustrated through image denoising in presence of additive white Gaussian noise, but the method can be used for other types of noise such as image-dependent noise as well. Finally, the results show that our method achieves comparable or slightly better results than existing approaches.

Image Denoising Inspired by Quantum Many-Body physics.

The main scope of this paper is to show how tools from quantum mechanics, in particular the Schroedinger equation, can be used to construct an adaptive transform suitable for signal and image processing applications. The proposed dictionary is obtained by considering the signal or image as a discrete potential in Schroedinger equation, further used to construct the Hamiltonien operator. In order to illustrate its practical interest in signal and image processing, we provide denoising results in the case of signal-dependent noise, which is the noise type the most adapted to the proposed approach.

Adaptive transform via quantum signal processing: application to signal and image denoising

Existing ultrasound deconvolution approaches unrealistically assume, primarily for computational reasons, that the convolution model relies on a spatially invariant kernel and circulant boundary conditions. We discard both restrictions and introduce an image formation model applicable to ultrasound imaging and deconvolution based on an axially varying kernel, which accounts for arbitrary boundary conditions. Our model has the same computational complexity as the one employing spatially invariant convolution and has negligible memory requirements. To accommodate the state-of-the-art deconvolution approaches when applied to a variety of inverse problem formulations, we also provide an equally efficient adjoint expression for our model. Simulation results confirm the tractability of our model for the deconvolution of large images. Moreover, in terms of accuracy metrics, the quality of reconstruction using our model is superior to that obtained using spatially invariant convolution.

/pdf/an-axially-variant-kernel-imaging-model-applied-to-qu9qmws361.pdf

Denis Kouame

Papers

Outlier detection at the parcel-level in wheat and rapeseed crops using multispectral and SAR time series

Quantum Mechanics-Based Signal and Image Representation: Application to Denoising

Image Denoising Inspired by Quantum Many-Body physics.

Adaptive transform via quantum signal processing: application to signal and image denoising

An Axially Variant Kernel Imaging Model Applied to Ultrasound Image Reconstruction