scholarly journals Joint Dedusting and Enhancement of Top-Coal Caving Face via Single-Channel Retinex-Based Method with Frequency Domain Prior Information

Symmetry ◽  
2021 ◽  
Vol 13 (11) ◽  
pp. 2097
Author(s):  
Chengcai Fu ◽  
Fengli Lu ◽  
Xiaoxiao Zhang ◽  
Guoying Zhang
Keyword(s):  
Frequency Domain ◽  
Prior Information ◽  
Single Channel ◽  
Coal Dust ◽  
Low Frequency ◽  
Frequency Component ◽  
White Balance ◽  
Local Means ◽  
Non Local ◽  
Color Restoration

Affected by the uneven concentration of coal dust and low illumination, most of the images captured in the top-coal caving face have low definition, high haze and serious noise. In order to improve the visual effect of underground images captured in the top-coal caving face, a novel single-channel Retinex dedusting algorithm with frequency domain prior information is proposed to solve the problem that Retinex defogging algorithm cannot effectively defog and denoise, simultaneously, while preserving image details. Our work is inspired by the simple and intuitive observation that the low frequency component of dust-free image will be amplified in the symmetrical spectrum after adding dusts. A single-channel multiscale Retinex algorithm with color restoration (MSRCR) in YIQ space is proposed to restore the foggy approximate component in wavelet domain. After that the multiscale convolution enhancement and fast non-local means (FNLM) filter are used to minimize noise of detail components while retaining sufficient details. Finally, a dust-free image is reconstructed to the spatial domain and the color is restored by white balance. By comparing with the state-of-the-art image dedusting and defogging algorithms, the experimental results have shown that the proposed algorithm has higher contrast and visibility in both subjective and objective analysis while retaining sufficient details.

Analysis of Vibration Signals in Monitoring Titanium End Milling Process Using Triaxial Accelerometer

2021 ◽  
Author(s):  
John Henry Navarro-Devia ◽  
Dzung Viet Dao ◽  
Yun Chen ◽  
Huaizhong Li
Keyword(s):  
Titanium Alloys ◽  
Frequency Domain ◽  
Single Channel ◽  
End Milling ◽  
Cutting Parameters ◽  
Frequency Component ◽  
Spindle Speed ◽  
Triaxial Accelerometer ◽  
Vibration Signals ◽  
Time Frequency

Abstract Vibrations during milling of hard-to-cut materials can cause low productivity, inferior quality and short tool life. It is one of the common issues in the machining of hard-to-cut materials employed in aerospace applications, such as titanium alloys. This paper presents an analysis of the vibration signals in the 3 axes of movement during titanium end milling, under diverse cutting parameters, manipulating spindle speed and feed rate. Signals were obtained using a triaxial accelerometer and processed in MATLAB. The analysis was conducted in the frequency-domain and the time-frequency domain. The results show that high-frequency vibration could occur in any direction with different amplitudes. Response on each axis depends on spindle speed, feed, and type of milling. A frequency component continually appeared in each axis regardless of cutting conditions and is located near the natural frequencies. Finally, the triaxial accelerations were compared for the milling cases with a new and a worn tool. Results highlight the importance and need for continuous monitoring of vibration in the 3 axes, instead of only using a single-channel signal, providing experimental data which could expand knowledge relating to the milling of titanium alloys.

scholarly journals Reconstruction of MR Images using Sparse Signal Sequences in Frequency Domain

2020 ◽  
Vol 9 (3) ◽  
pp. 895-902
Keyword(s):  
Compressed Sensing ◽  
Frequency Domain ◽  
High Frequency ◽  
Low Frequency ◽  
Frequency Component ◽  
High Frequency Component ◽  
Sparse Signal ◽  
Reference Image ◽  
Signal Acquisition ◽  
Coarse Scale

A new strategy for signal acquisition has emerged called Compressed Sensing (CS). The compressed sensing has gained attention in the filed of computer science, electrical engineering and mathematics. The Compressed Sensing is a mathematical approach of reconstructing a signal that is acquired from the dimensionally reduced data coefficients/less number of samples i.e. less than the Niquist rate. The data coefficients are high frequency component and low frequency component. The high frequency components are due to the rapid changes in the images (edges) and low frequency correspond provide the coarse scale approximation of the image, i.e. fine continuos surface. The idea is to retain only coarse scale approximation of the image i.e. the significant components that constitute the compressed signal. This compressed signal is the sparse signal which is so helpful during medical scenarios. During the Medical Resonance Imaging (MRI) scans, the patient undergoes many kinds difficulties like uncomfortness, patients are afraid of the scanning devices, h/she cannot be stable or changing his body positions slightly. Due to all these reasons, there can be a chance of acquiring only the less number of samples during the process of MRI scan. Even though the numbers of samples are less than the Nyquist rate, the reconstruction is possible by using the compressed sensing technique. The work has been carried out in the frequency domain to achieve the sparsity. The comparative study is done on percentage of different levels of sparsity of the signal. This can be verified by using Peak Signal Noise Ratio (PSNR), Mean Square Error (MSE) and Structural similarity (SSIM) methods which are calculated between the reference image and the reconstructed image. The finite dimensional signal has a sparsity and compressible representation. This sparsified data can be recovered from small set of linear, non-adaptive measurements. The implementation is done by using MATLAB.

Adaptive frequency-domain non-local means for seismic random noise attenuation

Geophysics ◽  
2020 ◽  
pp. 1-76
Author(s):  
Hang Wang ◽  
Yangkang Chen
Keyword(s):  
Frequency Domain ◽  
Random Noise ◽  
Computational Cost ◽  
Weighted Average ◽  
Original Data ◽  
Noise Attenuation ◽  
Random Noise Attenuation ◽  
Local Means ◽  
Non Local ◽  
The Impact

Non-local means (NLM) is one of the classical patch-based methods for random noise attenuation. It assumes that there is a lot of redundant information existing in similar patches, which can be utilized to restore the original data. However, this method is computationally expensive due to a large number of overlapping patches. Besides, since this method uses a weighted average of patches to suppress noise, when applied to the data with complicated structure, the “average effect” may appear in the denoised results. We propose to implement the NLM in frequency-space domain, which can be called the adaptive frequency-domain non-local means (AFNLM). This novel strategy will significantly reduce the computational cost and improve the quality of the final result compared to the traditional NLM. Considering the impact of filtering parameters on the final result, we also build a mapping relationship between the noise strength and filtering parameters, which could help obtain the denoised result with less signal leakage. We provide both synthetic and field examples to show the superiority of this method over the traditional f– x prediction and NLM methods.

scholarly journals Frequency domain despeckling technique for medical ultrasound images

2021 ◽  
Vol 72 (4) ◽  
pp. 229-239
Author(s):  
Jawad F. Al-Asad ◽  
Hiren K. Mewada ◽  
Adil H. Khan ◽  
Nidal Abu-Libdeh ◽  
Jamal F. Nayfeh
Keyword(s):  
Frequency Domain ◽  
Signal To Noise Ratio ◽  
Ultrasound Images ◽  
Medical Ultrasound ◽  
Schur Decomposition ◽  
Visual Effects ◽  
Local Means ◽  
Non Local ◽  
The Time Domain

Abstract This work proposes a novel frequency domain despeckling technique pertaining to the enhancement of the quality of medical ultrasound images. The results of the proposed method have been validated in comparison to both the time-domain and the frequency-domain projections of the schur decomposition as well as with several other benchmark schemes such as frost, lee, probabilistic non-local means (PNLM) and total variation filtering (TVF). The proposed algorithm has shown significant improvements in edge detection and signal to noise ratio (SNR) levels when compared with the performance of the other techniques. Both real and simulated medical ultrasound images have been used to evaluate the numerical and visual effects of each algorithm used in this work.

scholarly journals A phase-locked loop using ESO-based loop filter for grid-connected converter: performance analysis

2021 ◽  
Author(s):  
Baoling Guo ◽  
Seddik Bacha ◽  
Mazen Alamir ◽  
Julien Pouget
Keyword(s):  
Performance Analysis ◽  
Frequency Domain ◽  
Low Frequency ◽  
Experimental Tests ◽  
Frequency Measurement ◽  
Phase Locked Loop ◽  
Frequency Domain Analysis ◽  
Loop Filter ◽  
Control Stability ◽  
Unbalanced Grid

AbstractAn extended state observer (ESO)-based loop filter is designed for the phase-locked loop (PLL) involved in a disturbed grid-connected converter (GcC). This ESO-based design enhances the performances and robustness of the PLL, and, therefore, improves control performances of the disturbed GcCs. Besides, the ESO-based LF can be applied to PLLs with extra filters for abnormal grid conditions. The unbalanced grid is particularly taken into account for the performance analysis. A tuning approach based on the well-designed PI controller is discussed, which results in a fair comparison with conventional PI-type PLLs. The frequency domain properties are quantitatively analysed with respect to the control stability and the noises rejection. The frequency domain analysis and simulation results suggest that the performances of the generated ESO-based controllers are comparable to those of the PI control at low frequency, while have better ability to attenuate high-frequency measurement noises. The phase margin decreases slightly, but remains acceptable. Finally, experimental tests are conducted with a hybrid power hardware-in-the-loop benchmark, in which balanced/unbalanced cases are both explored. The obtained results prove the effectiveness of ESO-based PLLs when applied to the disturbed GcC.

scholarly journals Application of Fast Non-Local Means Algorithm for Noise Reduction Using Separable Color Channels in Light Microscopy Images

2021 ◽  
Vol 18 (6) ◽  
pp. 2903
Author(s):  
Seong-Hyeon Kang ◽  
Ji-Youn Kim
Keyword(s):  
Light Microscopy ◽  
Noise Reduction ◽  
Natural Image ◽  
Search Window ◽  
Local Means ◽  
Image Characteristics ◽  
Non Local ◽  
Spatial Quality ◽  
Evaluation Parameters ◽  
Color Channels

The purpose of this study is to evaluate the various control parameters of a modeled fast non-local means (FNLM) noise reduction algorithm which can separate color channels in light microscopy (LM) images. To achieve this objective, the tendency of image characteristics with changes in parameters, such as smoothing factors and kernel and search window sizes for the FNLM algorithm, was analyzed. To quantitatively assess image characteristics, the coefficient of variation (COV), blind/referenceless image spatial quality evaluator (BRISQUE), and natural image quality evaluator (NIQE) were employed. When high smoothing factors and large search window sizes were applied, excellent COV and unsatisfactory BRISQUE and NIQE results were obtained. In addition, all three evaluation parameters improved as the kernel size increased. However, the kernel and search window sizes of the FNLM algorithm were shown to be dependent on the image processing time (time resolution). In conclusion, this work has demonstrated that the FNLM algorithm can effectively reduce noise in LM images, and parameter optimization is important to achieve the algorithm’s appropriate application.

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