scholarly journals Conditional Noise Filter for MRI Images with Revised Theory on Second-order Histograms

2021 ◽  
Vol 3 ◽  
pp. 25-32
Author(s):  
Wai Ti Chan
Keyword(s):  
Noise Reduction ◽  
Ground Truth ◽  
Second Order ◽  
False Negatives ◽  
Noise Filter ◽  
Low Levels ◽  
Average Filter

Previous research by the author has the theory that histograms of second-order derivatives are capable of determining differences between pixels in MRI images for the purpose of noise reduction without having to refer to ground truth. However, the methodology of the previous research resulted in significant false negatives in determining which pixel is affected by noise. The theory has been revised in this article through the introduction of an additional Laplace curve, leading to comparisons between the histogram profile and two curves instead of just one. The revised theory is that differences between the first curve and the histogram profile and the differences between the second curve and the profile can determine which pixels are to be selected for filtering in order to improve image clarity while minimizing blurring. The revised theory is tested with a modified average filter versus a generic average filter, with PSNR and SSIM for scoring. The results show that for most of the sample MRI images, the theory of pixel selection is more reliable at higher levels of noise but not as reliable at preventing blurring at low levels of noise.

scholarly journals Generative Adversarial Network-Based Super-Resolution Considering Quantitative and Perceptual Quality

Symmetry ◽  
2020 ◽  
Vol 12 (3) ◽  
pp. 449 ◽  
Author(s):  
Can Li ◽  
Liejun Wang ◽  
Shuli Cheng ◽  
Naixiang Ao
Keyword(s):  
Super Resolution ◽  
Ground Truth ◽  
Second Order ◽  
Attention Mechanism ◽  
Generative Adversarial Networks ◽  
Perceptual Quality ◽  
Human Visual Perception ◽  
Image Perception ◽  
Generative Adversarial Network ◽  
Image Super Resolution

In recent years, the common algorithms for image super-resolution based on deep learning have been increasingly successful, but there is still a large gap between the results generated by each algorithm and the ground-truth. Even some algorithms that are dedicated to image perception produce more textures that do not exist in the original image, and these artefacts also affect the visual perceptual quality of the image. We believe that in the existing perceptual-based image super-resolution algorithm, it is necessary to consider Super-Resolution (SR) image quality, which can restore the important structural parts of the original picture. This paper mainly improves the Enhanced Super-Resolution Generative Adversarial Networks (ESRGAN) algorithm in the following aspects: adding a shallow network structure, adding the dual attention mechanism in the generator and the discriminator, including the second-order channel mechanism and spatial attention mechanism and optimizing perceptual loss by adding second-order covariance normalization at the end of feature extractor. The results of this paper ensure image perceptual quality while reducing image distortion and artefacts, improving the perceived similarity of images and making the images more in line with human visual perception.

Adaptive Myriad Filter for Improved Gas Turbine Condition Monitoring Using Transient Data

2005 ◽  
Vol 127 (2) ◽  
pp. 329-339 ◽  
Author(s):  
Vellore P. Surender ◽  
Ranjan Ganguli
Keyword(s):  
Noise Reduction ◽  
Gas Turbine ◽  
Health Monitoring ◽  
Moving Average ◽  
Acceleration Process ◽  
Outlet Temperature ◽  
Outlier Removal ◽  
Moving Average Filter ◽  
Average Filter

The removal of noise and outliers from measurement signals is a major problem in jet engine health monitoring. In this study, we look at the myriad filter as a substitute for the moving average filter that is widely used in the gas turbine industry. The three ideal test signals used in this study are the step signal that simulates a single fault in the gas turbine, while ramp and quadratic signals simulate long term deterioration. Results show that the myriad filter performs better in noise reduction and outlier removal when compared to the moving average filter. Further, an adaptive weighted myriad filter algorithm that adapts to the quality of incoming data is studied. The filters are demonstrated on simulated clean and deteriorated engine data obtained from an acceleration process from idle to maximum thrust condition. This data was obtained from published literature and was simulated using a transient performance prediction code. The deteriorated engine had single component faults in the low pressure turbine and intermediate pressure compressor. The signals are obtained from T2 (IPC total outlet temperature) and T6 (LPT total outlet temperature) engine sensors with their nonrepeatability values that were used as noise levels. The weighted myriad filter shows even greater noise reduction and outlier removal when compared to the sample myriad and a FIR filter in the gas turbine diagnosis. Adaptive filters such as those considered in this study are also useful for online health monitoring, as they can adapt to changes in quality of incoming data.

scholarly journals Second order noise shaping for data-weighted averaging technique to improve sigma-delta DAC performance

2021 ◽  
Vol 10 (1) ◽  
pp. 79
Author(s):  
Ali Kerem Nahar ◽  
Ansam Subhi Jaddar ◽  
Hussain K. Khleaf ◽  
Mohmmed Jawad Mortada Mobarek
Keyword(s):  
Power Dissipation ◽  
Second Order ◽  
Weighted Averaging ◽  
Noise Shaping ◽  
Sigma Delta ◽  
Averaging Technique ◽  
Delta Sigma ◽  
Low Levels ◽  
Digital To Analog ◽  
Data Weighted Averaging

<p>In general, the noise shaping responses, a cyclic second order response is delivered by the method of data weighted averaging (DWA) in which the output of the digital-to-analog convertor (DAC) is restricted to one of two states. DWA works efficiently for rather low levels of quantizing; it begins presenting considerable difficulties when internal levels of quantizing are extended further. Though, each added bit of internal quantizing causes an exponentially increasing in power dissipation, complexity and size of the DWA logic and the DAC. This gives a controlled seconnd order response accounting for the mismatch of the elements of DAC. The multi-bit DAC is made up of numerous single-bit DACs having values thereof chosen via a digital encoder. This research presents a discussion of the influence of mismatching between unit elements of the Delta-Sigma DAC. This results in a constrained second order response accounting for mismatch of DAC elements. The results of the simulation showed how the effectiveness of DWA method is in reducing band tones. Furthermore, DWA method has proved its efficiency in solving the mismatching of DAC unit elements. The noise of the mismatching elements is enhanced 11 dB at 0.01 with the proposed DWA, thereby enhancing the efficiency of the DAC in comparison to the efficiency of the DAC with no use of the circuit of DWA</p>

Adaptive Myriad Filter for Improved Gas Turbine Condition Monitoring Using Transient Data

2004 ◽  
Author(s):  
Vellore P. Surender ◽  
Ranjan Ganguli
Keyword(s):  
Noise Reduction ◽  
Gas Turbine ◽  
Health Monitoring ◽  
Moving Average ◽  
Acceleration Process ◽  
Outlet Temperature ◽  
Outlier Removal ◽  
Moving Average Filter ◽  
Average Filter

The removal of noise and outliers from measurement signals is a major problem in jet engine health monitoring. In this study, we look at the myriad filter as a substitute for the moving average filter which is widely used in the gas turbine industry. The three ideal test signals used in this study are the step signal which simulates a single fault in gas turbine, while ramp and quadratic signals simulate long term deterioration. Results show that the myriad filter performs better in noise reduction and outlier removal when compared to the moving average filter. Further, an adaptive weighted myriad filter algorithm that adapts to the quality of incoming data is studied. The filters are demonstrated on simulated clean and deteriorated engine data obtained from an acceleration process from idle to maximum thrust condition. This data was obtained from published literature and was simulated using a transient performance prediction code. The deteriorated engine had single component faults in the low pressure turbine and intermediate pressure compressor. The signals are obtained from T2 (IPC total outlet temperature) and T6 (LPT total outlet temperature) engine sensors with their non-repeatability values which were used as noise levels. The weighted myriad filter shows even greater noise reduction and outlier removal when compared to the sample myriad and FIR filter in the gas turbine diagnosis. Adaptive filters such as those considered in this study are also useful for online health monitoring as they can adapt to changes in quality of incoming data.

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