Resolution Improvement Method Research Articles

Spatial resolution improvement method of RDTS assisted by small-scale thermal region length recognition model

The Total Variation Deconvolution (TVD) algorithm plays an important role in signal reconstruction, however, when it is used to improve the spatial resolution of Raman Distributed Temperature Sensor (RDTS), there are certain challenges in parameter settings. This paper proposes to use Fully-Connected Neural Network to identify the length of small-scale thermal regions(SSTR), and based on the recognition results to set the TVD parameters automatically. We constructed training sets based on the periodic changes of SSTR signals in RDTS (which we call Thermal Region Response Modes, TRRM), to verify performance, we conducted comparative experiments between models obtained from a training set containing 100 types of TRRMs and 25 types of TRRMs, the Macro-F1 value of the former one is 0.2749 higher, reaching 0.9087, performed well in SSTR length recognition tasks. the traditional TVD assisted by this model can increase the spatial resolution of RDTS from 1.6 m to 0.4 m without manual intervention, which complements the lack of automation in applications of TVD and has practical value.

A Comprehensive Evaluation of Generalizability of Deep Learning-Based Hi-C Resolution Improvement Methods.

Hi-C is a widely used technique to study the 3D organization of the genome. Due to its high sequencing cost, most of the generated datasets are of a coarse resolution, which makes it impractical to study finer chromatin features such as Topologically Associating Domains (TADs) and chromatin loops. Multiple deep learning-based methods have recently been proposed to increase the resolution of these datasets by imputing Hi-C reads (typically called upscaling). However, the existing works evaluate these methods on either synthetically downsampled datasets, or a small subset of experimentally generated sparse Hi-C datasets, making it hard to establish their generalizability in the real-world use case. We present our framework-Hi-CY-that compares existing Hi-C resolution upscaling methods on seven experimentally generated low-resolution Hi-C datasets belonging to various levels of read sparsities originating from three cell lines on a comprehensive set of evaluation metrics. Hi-CY also includes four downstream analysis tasks, such as TAD and chromatin loops recall, to provide a thorough report on the generalizability of these methods. We observe that existing deep learning methods fail to generalize to experimentally generated sparse Hi-C datasets, showing a performance reduction of up to 57%. As a potential solution, we find that retraining deep learning-based methods with experimentally generated Hi-C datasets improves performance by up to 31%. More importantly, Hi-CY shows that even with retraining, the existing deep learning-based methods struggle to recover biological features such as chromatin loops and TADs when provided with sparse Hi-C datasets. Our study, through the Hi-CY framework, highlights the need for rigorous evaluation in the future. We identify specific avenues for improvements in the current deep learning-based Hi-C upscaling methods, including but not limited to using experimentally generated datasets for training.

A novel physics-based resolution improvement method for neutron-density log

In the later stages of oilfield development, the accurate identification of thin reservoirs become increasingly crucial for oil and gas exploration. However, conventional logs have limited vertical resolution, making it challenging to identify thin reservoirs. This results in potential hydrocarbon layers getting overlooked during exploration. To address this issue, a novel resolution enhancement method for density and neutron porosity logs based on physical constraint optimization is proposed. The proposed method involves three main steps: (1) developing an objective function that relates the true formation density and porosity to the measurement based on the physics of nuclear, (2) setting density neutron cross-constraints using the volumetric model of logging interpretation, and (3) establishing the objective function matrix equations using sliding window technology and solving the matrix equations using the constrained least square method to obtain high-resolution density and neutron porosity logs. The proposed method is evaluated using both synthetic and field logs obtained from compensated density and compensated neutron tools. The results demonstrate an effective improvement in neutron porosity and density log resolution to 15 cm, which is significantly enhanced compared to the typical vertical resolution of 2 ft. This study offers a new perspective for enhancing the resolution of logs and contributes to the exploration and development of thin oil and gas resources. And the proposed method could be used in future exploration and production projects to identify potential hydrocarbon layers that might have been overlooked by conventional logs.

Experimental research on temperature field construction of nuclear waste barrel based on RDTS with improved spatial resolution

In this paper, a 3D temperature field construction method based on Raman Distributed Temperature Sensor (RDTS) with improved spatial resolution is proposed to monitor the side surface temperature of Nuclear Waste Barrel (NWB). This method includes two aspects: the spatial resolution enhancement of RDTS and the signal dimension conversion. Firstly, the spatial resolution improvement method combining Fully-Connected Neural Network (FCNN) and Total Variation Deconvolution (TVD) is used to improve the spatial resolution of RDTS from 1.6 m to 0.4 m. Then, the one-dimensional temperature signal is converted into 2D temperature field by matrix laying method. Finally, by establishing the coordinate system of the NWB, the 2D temperature field can be converted into a 3D one. The experimental results of the simulation device showed that when the circular thermal area with diameter of 0.4 m, 0.6 m and 0.8 m appeared on the side surface of NWB, the temperature accuracy of the temperature field image constructed by this method can reach 80.91 %, 72.09 % and 85.97 % respectively, and the shape was accurate. The method proposed in this paper can accurately present the surface temperature field of NWB and provide technical support for the safe operation of the nuclear waste disposal repository during its life cycle.

Resolution Improvement in a High-Power Magnetic Resonance Imaging Gradient Power Amplifier

High-resolution gradient power amplifiers are required to generate high-fidelity gradient fields in magnetic resonance imaging (MRI) systems. Various aspects of gradient power amplifiers have been the subject of research in the past years, however, systematic analysis and design methods for its resolution improvement have not been sufficiently addressed. This article addresses this research gap with comprehensive analysis and design methods for resolution improvement. First, a method for systematic resolution characterization of the MRI gradient power amplifier with gradient coil is proposed. Second, noise modeling with respect to the critical aspects in the system resolution degradation is developed and discussed. Third, the resolution improvement methods of bandwidth optimization, pseudorandom code modulation, and utilizing silicon carbide (SiC) switching power devices are proposed and analyzed, achieving a resolution at the one part in <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">${10^6}$</tex-math></inline-formula> level. Finally, the resolution improvement methods are experimentally validated in a 1000 V/400 A SiC gradient power amplifier. The experimental results show a 3.6 times reduction in the peak spurious signals and resolution improvement at all current levels and pulse lengths, compared to a conventional insulated gate bipolar transistor GPA. These results prove the validity of the proposed methods for resolution improvement in a high-power MRI gradient power amplifier, enabling future high-power and high-resolution amplifier designs.

Improving the resolution of UAV-based remote sensing data of water quality of Lake Hachiroko, Japan by neural networks

Remote sensing techniques for periodically collecting global data have been widely used for water quality monitoring. Satellites with a ground resolution of 10 to 30 m obtain information over broad areas, making it difficult to use them to evaluate local water quality. Methods for improving satellite data resolution allow for water quality monitoring over both wide and local areas. However, previous studies have failed to target water bodies that undergo drastic changes; moreover, they have not sufficiently examined features contributing to resolution improvement. This study proposes a resolution improvement method using a neural network, which performs learning so that the output matches the high-resolution data when the target pixel and its surrounding pixels in the low-resolution data are input. Moreover, the band ratio of data obtained from an unmanned aerial vehicle was used in the learning process as an input feature. We investigated the (i) band ratio providing highly accurate resolution improvement and (ii) application of a new resolution improvement method to the estimation of suspended solid conditions for water quality parameters. Finally, the proposed method was compared with the bicubic method for validation. The results indicate that the estimated map at the band ratio B/R in the resolution improvement data created via the proposed method can be used to greatly improve the resolution in areas with high levels of suspended solids, compared to the water quality estimation maps created using the bicubic method.

Do Radiographic Assessments of Periodontal Bone Loss Improve with Deep Learning Methods for Enhanced Image Resolution?

Resolution plays an essential role in oral imaging for periodontal disease assessment. Nevertheless, due to limitations in acquisition tools, a considerable number of oral examinations have low resolution, making the evaluation of this kind of lesion difficult. Recently, the use of deep-learning methods for image resolution improvement has seen an increase in the literature. In this work, we performed two studies to evaluate the effects of using different resolution improvement methods (nearest, bilinear, bicubic, Lanczos, SRCNN, and SRGAN). In the first one, specialized dentists visually analyzed the quality of images treated with these techniques. In the second study, we used those methods as different pre-processing steps for inputs of convolutional neural network (CNN) classifiers (Inception and ResNet) and evaluated whether this process leads to better results. The deep-learning methods lead to a substantial improvement in the visual quality of images but do not necessarily promote better classifier performance.

Virtual single-pixel imaging-based deconvolution method for spatial resolution improvement in wide-field fluorescence microscopy.

Deconvolution technique has been widely used in fluorescence microscopy to restore fine structures of biological samples. However, conventional deconvolution methods usually achieve little contrast enhancement in dense structures that have the intervals close to the Rayleigh criterion. Herein, we developed a novel deconvolution method, termed virtual single-pixel imaging (v-SPI). Differing from existing deconvolution methods, v-SPI aims to retrieve the less blurred image directly, not the sample distribution which cannot be actually obtained. And the result can be retrieved simply by solving a linear matrix in spatial domain. In addition, the proposed method has no requirement of calibrating parameters of microscope system. Simulation and experimental results demonstrated that the proposed v-SPI method can enhance the contrast of dense structures significantly and acquire a 24% increase in resolution.

High-Resolution Radar Waveform Design Based on Target Information Maximization

Although the transmit radar waveform design problem for maximizing target information has been studied widely in the past, the resolution requirement is normally ignored in such designs. Using maximizing target information as a criterion, a new radar waveform design method meeting the high-resolution requirement is proposed in this article, which makes no assumptions on the statistical distribution of target scattering. The objective function is proposed by maximizing the Pearson correlation coefficient and the design is then transformed into an optimization problem, which is solved in two steps. First, a closed-form expression for the discretized waveform with constant power constraint is derived in the time domain. Second, based on the bandwidth analysis of the optimal solution, a resolution improvement method considering information distortion is introduced and a suboptimal waveform is proposed while satisfying the constant power and resolution requirements. Finally, performance of the proposed radar waveform in terms of information acquisition, classification, and resolution is analyzed and compared with the classic high-resolution linear frequency modulated waveform (LFMW). Simulation results show that the resolution of the suboptimal waveform is slightly lower than the LFMW, but more desirable in terms of peak sidelobe ratio, information acquisition, and classification.

The application of harmonic techniques to enhance resolution in mesh-based x-ray phase imaging

X-ray phase-contrast imaging produces significantly higher contrast than the conventional attenuation-based imaging. However, phase contrast typically requires small low-power sources or precisely machined and aligned gratings to generate the required spatial coherence. To overcome this limitation, we use a simple wire mesh to produce a periodic intensity pattern in the illumination. Distortions in this pattern upon propagation through an object can be used to produce phase contrast images. Unlike Talbot-grating-based techniques, this directly provides a measure of the phase gradient. Due to periodicity, fast Fourier transforms enable rapid retrieval of the phase contrast image. The high contrast of the mesh pattern allows a significant relaxation of the coherence requirement. However, the resolution of the phase contrast image is normally limited by the mesh period. We demonstrate that employing wider windowing functions during the Fourier processing substantially improves the spatial resolution at the cost of introducing artifacts in the reconstructed images. In order to remove these artifacts, a method is presented to combine images acquired while spatially shifting the mesh. Experimental results demonstrate the successful removal of these artifacts. This offers a method for significant resolution improvement in mesh-based phase-contrast imaging, enabling high resolution phase contrast images with a significant relaxation of source coherence requirements.

A signal domain transform method for spatial resolution improvement of Lamb wave signals with synthetically measured relative wavenumber curves

In practical structural health monitoring with Lamb waves, the signal spatial resolution is usually restricted by not only dispersion but also the space duration of excitation waveforms, that is, the initial spatial resolution for the signals before traveling. As a result, the final resolution and accuracy of damage identification could be badly impaired. To overcome this problem, a signal domain transform method is presented in this article. In signal domain transform, the original dispersive Lamb wave signals are transformed from the time to distance domains, with the time–distance scaling on the excitation waveforms particularly modified. Then, both dispersion compensation and initial spatial resolution enhancement can be actualized to efficiently improve the signal spatial resolution. Considering the practical situation that the structural property parameters could be unavailable to theoretically derive the requisite wavenumber relations, signal domain transform with synthetically measured relative wavenumber curves is further explored. After the frequency domain representation and spatial resolution of Lamb wave signals are basically analyzed, the principle and numerical realization of signal domain transform are investigated. Hereafter, the synthetic measurement of relative wavenumber curves for signal domain transform is discussed and preliminarily validated in an aluminum plate. Finally, signal domain transform is applied for high-resolution imaging of adjacent multiple damages. The efficiency of signal domain transform and signal domain transform–based imaging methods has been well demonstrated by the experimental study on a glass fiber–reinforced composite plate with unknown material parameters.

A sparse representation-based image resolution improvement method by processing multiple dictionary pairs with latent Dirichlet allocation model for street view images

Street view applications are widely used in many situations. However, the resolution of the street view image is not high enough. Users always desire high resolution street view images. Image resolution improvement methods can effectively generate a high resolution street view image from a single low resolution street view image. The sparse representation-based image resolution improvement method is a promising way to improve the resolution of an image. However, only one dictionary pair, which fails to represent the diverse structures in images, is used in conventional sparse representation-based methods This may lead to poor performances in many circumstances. In this paper, we propose a new sparse representation-based method with multiple dictionary pairs. To capture the various structures at the semantic level, our method adopts latent Dirichlet allocation model to divide the patches into clusters. Then we learn a dictionary pair for each cluster. Finally, these dictionary pairs are used to reconstruct high resolution images. Experimental results validate that our method is superior over the compared methods in both visual perception and objective quantitation.

Parameters optimization of the novel probabilistic algorithm for improving spatial resolution of multispectral satellite images

A probabilistic method for improving the spatial resolution of multispectral space images using a reference image is proposed. The developed method calculates the mathematical expectation of pixel brightness in different channels of an improved multispectral image based on the probabilistic characteristics of the pixel neighborhood on the multispectral image and the overall brightness intensity of the panchromatic image at that point. The applicability of different metrics for evaluating the quality of the spatial resolution of satellite images is analyzed. A set of the most adequate quality evaluation metrics is used. An optimization procedure is developed to adjust the parameters of the proposed probabilistic resolution improvement method. The results of testing the method on the multi-spectral images obtained from different satellites in different spatial resolutions are presented. The efficiency of the algorithm is tested at different magnification scales. A comparative analysis of the results of the proposed method with similar approaches is conducted.

Improving the resolution of seismic traces based on the secondary time–frequency spectrum

The resolution of seismic data is critical to seismic data processing and the subsequent interpretation of fine structures. In conventional resolution improvement methods, the seismic data is assumed stationary and the noise level not changes with space, whereas the actual situation does not satisfy this assumption, so that results after resolution improvement processing is not up to the expected effect. To solve these problems, we propose a seismic resolution improvement method based on the secondary time–frequency spectrum. First, we propose the secondary time-frequency spectrum based on S transform (ST) and discuss the reflection coefficient sequence and time-dependent wavelet in the secondary time–frequency spectrum. Second, using the secondary time–frequency spectrum, we design a twodimensional filter to extract the amplitude spectrum of the time-dependent wavelet. Then, we discuss the improvement of the resolution operator in noisy environments and propose a novel approach for determining the broad frequency range of the resolution operator in the time–frequency–space domain. Finally, we apply the proposed method to synthetic and real data and compare the results of the traditional spectrum-modeling deconvolution and Q compensation method. The results suggest that the proposed method does not need to estimate the Q value and the resolution is not limited by the bandwidth of the source. Thus, the resolution of the seismic data is improved sufficiently based on the signal-to-noise ratio (SNR).

Resolution enhancement of seismic data using spectral modeling based on dominant Ricker components and separable nonlinear least squares

Seismic resolution enhancement has a key role in seismic interpretation, especially in reservoir characterization and geologic interpretation. Exploration seismology has a limitation in temporal resolution and thin-layer detection, so any effort to improve the resolution of available data is valuable. Seismic deconvolution is one of the main steps in seismic processing and is intensively used for enhancing the vertical resolution. In this procedure, the seismic wavelet is compressed in order to decrease wavelet interferences and improve temporal resolution. In the common method of seismic deconvolution — i.e., Wiener deconvolution — the seismic wavelet is estimated by using the early part of autocorrelation of traces. Assuming the reflection series is a random function, the smoothed version of the amplitude spectrum of traces can be considered as the amplitude spectrum of the seismic wavelet. The wavelet spectrum can be properly reconstructed by the linear combination of a number of Ricker wavelets with different peak frequencies. The authors aim to introduce a novel method for resolution improvement based on fitting and finding main Ricker components of the source-wavelet spectrum. This attempt is classified under the group of the separable nonlinear least squares (SNLS), the objective function of which is a combination of linear and nonlinear functions. A variable projection method is applied to recognize the optimum peak frequency of Ricker wavelets involved in the wavelet spectrum. Our synthetic tests indicate that the SNLS algorithm is able to achieve a very close approximation of dominant Ricker components of the source spectrum. Considering the minimum phase assumption of the wavelet, the phase spectrum is also computed by Hilbert transform of the estimated amplitude spectrum. Once the amplitude and phase information are available, the deconvolution operator can be designed. Our synthetic and real tests indicate that, in comparison with Wiener deconvolution, spectral modeling based on dominant Ricker components (SMDRC) has improved the deconvolution outputs.

Range resolution improvement in FM-based passive radars using deconvolution

FM-based passive bistatic radar (PBR) systems suffer from low range resolution because of the low baseband bandwidth of commercial FM broadcasts. In this paper, we propose a range resolution improvement method using deconvolution. The output of the PBR matched filter is processed using a deconvolution algorithm which assumes that targets are isolated, i.e., sparse in the range domain. The deconvolution algorithm is iterative and was implemented by performing successive orthogonal projections onto supporting hyperplanes of the epigraph set of a convex cost function. Simulation examples are presented.

Enhanced temporal resolution at cardiac CT with a novel CT image reconstruction algorithm: Initial patient experience

ObjectiveTo evaluate the effect of a temporal resolution improvement method (TRIM) for cardiac CT on diagnostic image quality for coronary artery assessment. Materials and methodsThe TRIM-algorithm employs an iterative approach to reconstruct images from less than 180° of projections and uses a histogram constraint to prevent the occurrence of limited-angle artifacts. This algorithm was applied in 11 obese patients (7 men, 67.2±9.8 years) who had undergone second generation dual-source cardiac CT with 120kV, 175–426mAs, and 500ms gantry rotation. All data were reconstructed with a temporal resolution of 250ms using traditional filtered-back projection (FBP) and of 200ms using the TRIM-algorithm. Contrast attenuation and contrast-to-noise-ratio (CNR) were measured in the ascending aorta. The presence and severity of coronary motion artifacts was rated on a 4-point Likert scale. ResultsAll scans were considered of diagnostic quality. Mean BMI was 36±3.6kg/m2. Average heart rate was 60±9bpm. Mean effective dose was 13.5±4.6mSv. When comparing FBP- and TRIM reconstructed series, the attenuation within the ascending aorta (392±70.7 vs. 396.8±70.1HU, p>0.05) and CNR (13.2±3.2 vs. 11.7±3.1, p>0.05) were not significantly different. A total of 110 coronary segments were evaluated. All studies were deemed diagnostic; however, there was a significant (p<0.05) difference in the severity score distribution of coronary motion artifacts between FBP (median=2.5) and TRIM (median=2.0) reconstructions. ConclusionThe algorithm evaluated here delivers diagnostic imaging quality of the coronary arteries despite 500ms gantry rotation. Possible applications include improvement of cardiac imaging on slower gantry rotation systems or mitigation of the trade-off between temporal resolution and CNR in obese patients.

SPECTRAL RESOLUTION IMPROVEMENT BY NON-DAMPING FILTER

Abstract A new method for the spectral resolution improvement is described. The difficulty that appears in the usual method for the resolution improvement is eliminated by use of the characteristic of the delta function. Results of the numerical simulation show the effectiveness of the proposed method using a non-damping filter function.

1001 X線CT画像の空間分解能向上手法に関する研究(OSI 3-1バイオエンジニアリング(1)一細胞・医療・福祉一)

1001 X線CT画像の空間分解能向上手法に関する研究(OSI 3-1バイオエンジニアリング(1)一細胞・医療・福祉一)

分光画像圧縮に基づいた分光ベースレンダリングの高精細化

分光画像圧縮に基づいた分光ベースレンダリングの高精細化