Image super-resolution

Under the alternating electrical excitation, biological tissues produce a complex electrical impedance which depends on tissue composition, structures, health status, and applied signal frequency, and hence the bioelectrical impedance methods can be utilized for noninvasive tissue characterization. As the impedance responses of these tissue parameters vary with frequencies of the applied signal, the impedance analysis conducted over a wide frequency band provides more information about the tissue interiors which help us to better understand the biological tissues anatomy, physiology, and pathology. Over past few decades, a number of impedance based noninvasive tissue characterization techniques such as bioelectrical impedance analysis (BIA), electrical impedance spectroscopy (EIS), electrical impedance plethysmography (IPG), impedance cardiography (ICG), and electrical impedance tomography (EIT) have been proposed and a lot of research works have been conducted on these methods for noninvasive tissue characterization and disease diagnosis. In this paper BIA, EIS, IPG, ICG, and EIT techniques and their applications in different fields have been reviewed and technical perspective of these impedance methods has been presented. The working principles, applications, merits, and demerits of these methods has been discussed in detail along with their other technical issues followed by present status and future trends.

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Bioelectrical Impedance Methods for Noninvasive Health Monitoring: A Review.

PURPOSE To develop a super-resolution technique using convolutional neural networks for generating thin-slice knee MR images from thicker input slices, and compare this method with alternative through-plane interpolation methods. METHODS We implemented a 3D convolutional neural network entitled DeepResolve to learn residual-based transformations between high-resolution thin-slice images and lower-resolution thick-slice images at the same center locations. DeepResolve was trained using 124 double echo in steady-state (DESS) data sets with 0.7-mm slice thickness and tested on 17 patients. Ground-truth images were compared with DeepResolve, clinically used tricubic interpolation, and Fourier interpolation methods, along with state-of-the-art single-image sparse-coding super-resolution. Comparisons were performed using structural similarity, peak SNR, and RMS error image quality metrics for a multitude of thin-slice downsampling factors. Two musculoskeletal radiologists ranked the 3 data sets and reviewed the diagnostic quality of the DeepResolve, tricubic interpolation, and ground-truth images for sharpness, contrast, artifacts, SNR, and overall diagnostic quality. Mann-Whitney U tests evaluated differences among the quantitative image metrics, reader scores, and rankings. Cohen's Kappa (κ) evaluated interreader reliability. RESULTS DeepResolve had significantly better structural similarity, peak SNR, and RMS error than tricubic interpolation, Fourier interpolation, and sparse-coding super-resolution for all downsampling factors (p < .05, except 4 × and 8 × sparse-coding super-resolution downsampling factors). In the reader study, DeepResolve significantly outperformed (p < .01) tricubic interpolation in all image quality categories and overall image ranking. Both readers had substantial scoring agreement (κ = 0.73). CONCLUSION DeepResolve was capable of resolving high-resolution thin-slice knee MRI from lower-resolution thicker slices, achieving superior quantitative and qualitative diagnostic performance to both conventionally used and state-of-the-art methods.

Super-resolution musculoskeletal MRI using deep learning.

Image reconstruction of compressed sensing MRI using graph-based redundant wavelet transform.

Simultaneous single- and multi-contrast super-resolution for brain MRI images based on a convolutional neural network.

Sparse representation-based MRI super-resolution reconstruction

Research on state of health prediction model for lithium batteries based on actual diverse data

The discriminative correlation filter (DCF) is one of the crucial visual tracking methods, and it has outstanding performance. Nevertheless, DCF-based methods have an unavoidable boundary effect, which results in poor tracking performance in an abrupt scene, such as fast motion or deformation. To address this problem, we propose a novel dynamic saliency discriminative correlation filter for visual tracking. In our approach, a response guided saliency map is constructed to introduce saliency information into the filter. The method effectively highlights the target by further increasing the number of positive samples to alleviate the boundary effect. We also investigate an effective multifeature integration method to extract the target feature by employing the Felzenszwalb Histograms of Oriented Gradients (fHOG) from each color space. Finally, we apply a novel update approach to prevent filter model degradation, which uses a temporal regularization term to update the filter model. Extensive experiments on the standard OTB-2015 benchmark validate that our approach achieves competitive performance compared to other state-of-the-art trackers. Moreover, we conducted an ablation study to evaluate the effectiveness of the components in our tracker.

Visual tracking via dynamic saliency discriminative correlation filter

In this paper, we propose a novel method for Super-Resolution Medical image based sparse representation, with the aim to solve the problem of MR image resolution owing to the limitations of hardware and acquisitions. With two coupled dictionaries the sparse representation of a low resolution medical image blocks is used to generate a high resolution. Some evaluations are implemented to compare with previous method, and the proposed algorithm has its advantage on super-resolution.

Medical Image Super-resolution Analysis with Sparse Representation

To effectively resolve the automatic recognition of analog pointer measuring instruments, a reading recognition method based on Hough transform is proposed. In order to simplify the following procedure, image graying and binary, mathematical morphology, including dilation and thinning, edge recognition was applied to the system. We proposed the recognition principle for reading recognition of the pointer's angle by using Hough transform. And the reading of the pointer is decided by the linear relationship between the panel's scale and the angle of the instrument's dial revolutionary. Considering that in most practical cases, the panel cannot be horizontal, so the procedure of tilt correction is employed in this paper. Finally, the software is written in MATLAB. The recognition system is achieved in LabVIEW, where the front panel of the reading system is developed. The experimental results demonstrate that our method is feasible for pointer instrument recognition.

Ping Fu

Papers

Sparse representation-based MRI super-resolution reconstruction

Research on state of health prediction model for lithium batteries based on actual diverse data

Visual tracking via dynamic saliency discriminative correlation filter

Medical Image Super-resolution Analysis with Sparse Representation

Development of an automatic reading method and software for pointer instruments