Quantum information processors promise fast algorithms for problems inaccessible to classical computers. But since qubits are noisy and error-prone, they will depend on fault-tolerant quantum error correction (FTQEC) to compute reliably. Quantum error correction can protect against general noise if—and only if—the error in each physical qubit operation is smaller than a certain threshold. The threshold for general errors is quantified by their diamond norm. Until now, qubits have been assessed primarily by randomized benchmarking, which reports a different error rate that is not sensitive to all errors, and cannot be compared directly to diamond norm thresholds. Here we use gate set tomography to completely characterize operations on a trapped-Yb+-ion qubit and demonstrate with greater than 95% confidence that they satisfy a rigorous threshold for FTQEC (diamond norm ≤6.7 × 10−4).

Demonstration of Qubit Operations Below a Rigorous Fault Tolerance Threshold With Gate Set Tomography (Open Access, Publisher's Version)

Image super-resolution: A comprehensive review, recent trends, challenges and applications

Multi-modality medical image fusion based on guided filter and image statistics in multidirectional shearlet transform domain

Over the past few years, a tremendous change has occurred in computer-aided diagnosis (CAD) technology. The evolution of numerous medical imaging techniques has enhanced the accuracy of the preliminary analysis of several diseases. Magnetic resonance imaging (MRI) is a prevalent technology extensively used in evaluating the progress of the spread of malignant tissues or abnormalities in the human body. This article aims to automate a computationally efficient mechanism that can accurately identify the tumor from MRI images and can analyze the impact of the tumor. The proposed model is robust enough to classify the tumors with minimal training data. The generative variational autoencoder models are efficient in reconstructing the images identical to the original images, which are used in adequately training the model. The proposed self-learning algorithm can learn from the insights from the autogenerated images and the original images. Incorporating long short-term memory (LSTM) is faster processing of the high dimensional imaging data, making the radiologist's task and the practitioners more comfortable assessing the tumor's progress. Self-learning models need comparatively less data for the training, and the models are more resource efficient than the various state-of-art models. The efficiency of the proposed model has been assessed using various benchmark metrics, and the obtained results have exhibited an accuracy of 89.7%. The analysis of the progress of tumor growth is presented in the current study. The obtained accuracy is not pleasing in the healthcare domain, yet the model is reasonably fair in dealing with a smaller size dataset by making use of an image generation mechanism. The study would outline the role of an autoencoder in self-learning models. Future technologies may include sturdy feature engineering models and optimized activation functions that would yield a better result.

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Variational Autoencoders-BasedSelf-Learning Model for Tumor Identification and Impact Analysis from 2-D MRI Images

Tri-modal medical image fusion based on adaptive energy choosing scheme and sparse representation

Medical imaging is an essential medical diagnosis system subsequently integrated with artificial intelligence for assistance in clinical diagnosis. The actual medical images acquired during the image capturing procedures generate poor quality images as a result of numerous physical restrictions of the imaging equipment and time constraints. Recently, medical image super-resolution (SR) has emerged as an indispensable research subject in the community of image processing to address such limitations. SR is a classical computer vision operation that attempts to restore a visually sharp high-resolution images from the degraded low-resolution images. In this study, an effective medical super-resolution approach based on weighted least squares optimisation via multiscale convolutional neural networks (CNNs) has been proposed for lesion localisation. The weighted least squares optimisation strategy that particularly is well-suited for progressively coarsening the original images and simultaneously extract multiscale information has been executed. Subsequently, a SR model by training CNNs based on wavelet analysis has been designed by carrying out wavelet decomposition of optimized images for multiscale representations. Then multiple CNNs have been trained separately to approximate the wavelet multiscale representations. The trained multiple convolutional neural networks characterize medical images in many directions and multiscale frequency bands, and thus facilitate image restoration subject to increased number of variations depicted in different dimensions and orientations. Finally, the trained CNNs regress wavelet multiscale representations from a LR medical images, followed by wavelet synthesis that forms a reconstructed HR medical image. The experimental performance indicates that the proposed model SR restoration approach achieve superior SR efficiency over existing comparative methods

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A weighted least squares optimisation strategy for medical image super resolution via multiscale convolutional neural networks for healthcare applications

Measurement and analysis of multi-modal image fusion metrics based on structure awareness using domain transform filtering

Image fusion is mandatory to enhance the object recognition in images by concatenating many sources of planetoid, aerial and ground-based imaging systems with other related data set. Image fusion is the methodology of overlapping at least a couple of pictures so that their combination enhances the overall information content. In literature, there are many algorithms, whose objective is the fusion of images to improve the characteristics of the final image. This work primarily presents a contrast between thee cosine harmonic transform of wavelet transform, guided filter algorithm and discrete form of Wavelet transform. It can be stated with simple observation that the image fusion techniques based on guided refining or masking retrieve a good visual resultant image. Their performance is evaluated on Standard “Gun” dataset with the help of three most widely used performance evaluation metrics. The experimentation and work done on MATLAB, validates the superiority of DCHWT based fusion algorithm over others.

Infrared and Visible Image Fusion for Concealed Weapon Detection using Transform and Spatial Domain Filters

Clinicians often have to switch amongst radiographic scans in order to trace out patterns in various tissue striations. The conglomerated view of structural and anatomical view in medical scans can facilitate the physicians to execute precise diagnosis, intraoperative guidance, and planning preoperative procedures. Due to inherent physical limitations, source images have prevalence of noise and ambient light. This results in lower contrast and limited visual perception of striations and tissues in fused radiographic images. This paper proposes a concatenated filtering image fusion approach employing space segmentation and non-prior-based contrast enhancement. The latent row rank theory approach implements sub-space segmentation addressing the issue of noise removal, and the non-local-prior-based enhancement removes the ambient light from source images fortifying edge details and information. This complex fusion framework is designed in non-sub-sampled contourlet transform which exhibits computational efficiency. The final fused image obtained using local Laplacian energy fusion rule results in improved localisation of structural and anatomical details of brain tissue and outperforms high-performing fusion methods in literature both objectively with high fusion rate along with better quality visual results.

Bhawna Goyal

Papers

A weighted least squares optimisation strategy for medical image super resolution via multiscale convolutional neural networks for healthcare applications

Measurement and analysis of multi-modal image fusion metrics based on structure awareness using domain transform filtering

Infrared and Visible Image Fusion for Concealed Weapon Detection using Transform and Spatial Domain Filters

An Efficient Medical Assistive Diagnostic Algorithm for Visualisation of Structural and Tissue Details in CT and MRI Fusion

An efficient medical image super resolution based on piecewise linear regression strategy using domain transform filtering