Single Image De-Hazing via Multiscale Wavelet Decomposition and Estimation with Fractional Gradient-Anisotropic Diffusion Fusion

2021 ◽  
pp. 2150032
Author(s):  
Uche A. Nnolim
Keyword(s):  
High Frequency ◽  
Anisotropic Diffusion ◽  
Wavelet Decomposition ◽  
Single Image ◽  
Nonlocal Means ◽  
Gradient Domain ◽  
Multi Level ◽  
Bilateral Filters ◽  
Approximation Coefficients ◽  
Experimental Comparisons

This paper presents algorithms based on fractional multiscale gradient fusion and multilevel wavelet decomposition for underwater and hazy image enhancement. The algorithms utilize partial differential equation (PDE)-generated low- and high-frequency images fused via gradient domain and anisotropic diffusion. Furthermore, wavelet multi-level decomposition, estimation and adjustment of detail and approximation coefficients are employed in improving local and global enhancement. Solutions to halo effect are also developed using compressive bilateral filters or other nonlinear/nonlocal means filter. Ultimately, experimental comparisons indicate that the proposed methods surpass or are comparable to several algorithms from the literature.

scholarly journals Reflexionseigenschaften von Windenergieanlagen im Funkfeld von Funknavigations- und Radarsystemen

2015 ◽  
Vol 13 ◽  
pp. 9-18 ◽  
Author(s):  
S. Sandmann ◽  
S. Divanbeigi ◽  
H. Garbe
Keyword(s):  
High Frequency ◽  
Physical Optics ◽  
Very High Frequency ◽  
Radio Range ◽  
Multi Level ◽  
Very High

Abstract. Die hier behandelte Untersuchung befasst sich mit den Störungen des elektrischen Feldes einer Doppler Very High Frequency Omnidirectional Radio Range Navigationsanlage (DVOR) in der Gegenwart von Windenergieanlagen (WEA). Hierfür wird die Feldstärke auf 25 konzentrischen Kreisbahnen, sog. Orbit Flights verschiedener Höhen und mit verschiedenen Radien rund um die DVOR-Anlage numerisch simuliert. Insbesondere werden die Einflüsse diverser Parameter der WEA wie deren Anzahl, Position, Rotorwinkel, Turmhöhe und Rotordurchmesser auf die Feldverteilung herausgestellt, sowie die Anwendbarkeit der Simulationsmethode Physical Optics (PO) durch Vergleich der Simulationsergebnisse mit denen der Multi Level Fast Multipol Method (MLFMM) untersucht.

scholarly journals TIME SERIES PROCESSING USING WAVELET-TRANSFORMATIONS FOR ACCURACY INCREASE IN INFORMATION PRESENTATION

2018 ◽  
Vol 2018 (8) ◽  
pp. 67-75
Author(s):  
Юрий Кропотов ◽  
Yuriy Kropotov ◽  
Алексей Белов ◽  
Aleksey Belov ◽  
Александр Проскуряков ◽  
...  
Keyword(s):  
Time Series ◽  
Wavelet Decomposition ◽  
Statistical Processing ◽  
Threshold Level ◽  
Information Presentation ◽  
Statistical Parameters ◽  
Noise Component ◽  
Low Frequencies ◽  
Accuracy Increase ◽  
Approximation Coefficients

The purpose of this work is development of the method for error decrease in information presentation in telecommunication systems of monitoring by means of filtering noise and fluctuations of levels in time series counts. To solve this problem there is used a method of wavelet processing. In particular, the decrease of time series fluctuation impact is carried out by means of the computation of approximating coefficients of the n-th level which corresponds to the fulfillment of multi-level statistical processing the values of time series counts and equivalent to a signal passage through a filter of low frequencies. There was developed and investigated a simulator and its statistical parameters of processing with a wavelet transformation of time series counts. It is shown that time series wavelet processing and the application of approximation coefficients of waveletdecomposition increase the accuracy of data presentation. It is also ensured at the expense of noise component suppression through a method of thresholding upon detailing coefficients of decomposition. In the paper there are shown investigations of the dependence of approximation coefficient correlation time upon a wavelet decomposition level. There was also investigated a depression dependence of noise components of time series count fluctuations of emission at the processing with the wavelet decomposition with obtaining approximation coefficients of different levels. The fulfilled analysis of the results of different criteria application and approaches to smoothing on the basis of threshold processing the detail coefficients of wavelet decomposition has shown that at smoothing time series there will be an optimum choice of an adaptive penalty threshold level. The presented results of smoothing with an adaptive penalty threshold have shown that the signal-noise ratio increased for more than 2.53dB in comparison with the initial one.

scholarly journals Pansharpening with a Gradient Domain GIF Based on NSST

Electronics ◽  
2019 ◽  
Vol 8 (2) ◽  
pp. 229
Author(s):  
Jiao Jiao ◽  
Lingda Wu
Keyword(s):  
High Frequency ◽  
Low Frequency ◽  
Fusion Rule ◽  
Objective Evaluation ◽  
Data Sets ◽  
Multi Scale ◽  
Guided Image Filter ◽  
Gradient Domain ◽  
Spatial Quality

In order to improve the fusion quality of multispectral (MS) and panchromatic (PAN) images, a pansharpening method with a gradient domain guided image filter (GIF) that is based on non-subsampled shearlet transform (NSST) is proposed. First, multi-scale decomposition of MS and PAN images is performed by NSST. Second, different fusion rules are designed for high- and low-frequency coefficients. A fusion rule that is based on morphological filter-based intensity modulation (MFIM) technology is proposed for the low-frequency coefficients, and the edge refinement is carried out based on a gradient domain GIF to obtain the fused low-frequency coefficients. For the high-frequency coefficients, a fusion rule based on an improved pulse coupled neural network (PCNN) is adopted. The gradient domain GIF optimizes the firing map of the PCNN model, and then the fusion decision map is calculated to guide the fusion of the high-frequency coefficients. Finally, the fused high- and low-frequency coefficients are reconstructed with inverse NSST to obtain the fusion image. The proposed method was tested using the WorldView-2 and QuickBird data sets; the subjective visual effects and objective evaluation demonstrate that the proposed method is superior to the state-of-the-art pansharpening methods, and it can efficiently improve the spatial quality and spectral maintenance.

Multi-Level Assessment of Fracture Calluses in Rats Subjected to Low-Magnitude High-Frequency Vibration with Different Rest Periods

2016 ◽  
Vol 44 (8) ◽  
pp. 2489-2504 ◽  
Author(s):  
Jiazi Gao ◽  
He Gong ◽  
Xu Huang ◽  
Rui Zhang ◽  
Renshi Ma ◽  
...  
Keyword(s):  
High Frequency ◽  
High Frequency Vibration ◽  
Rest Periods ◽  
Multi Level ◽  
Low Magnitude

scholarly journals Wavelet-based audio features of DC motor sound

2021 ◽  
Vol 34 (1) ◽  
pp. 71-88
Author(s):  
Djordje Damnjanovic ◽  
Dejan Ciric ◽  
Zoran Peric
Keyword(s):  
Fourier Transform ◽  
Signal Analysis ◽  
Wavelet Decomposition ◽  
Dc Motor ◽  
Feature Representation ◽  
Production Environment ◽  
Audio Features ◽  
The Fourier Transform ◽  
Approximation Coefficients ◽  
Motor Production

The usage of wavelets is widespread in many fields nowadays, especially in signal processing. Their nature provides some advantages in comparison to the Fourier transform, and therefore many applications rely on wavelets rather than on other methods. The decomposition of wavelets into detail and approximation coefficients is one of the methods to extract representative audio features. They can be used in signal analysis and further classification. This paper investigates the usage of various wavelet families in the wavelet decomposition to extract audio features of direct current (DC) motor sounds recorded in the production environment. The purpose of feature representation and analysis is the detection of DC motor failures in motor production. The effects of applying different wavelet families and parameters in the decomposition process are studied using sounds of more than 60 motors. Time and frequency analysis is also done for the tested DC motor sounds.

scholarly journals Signal processing of nonlinear dynamic systems

2021 ◽  
Vol 2094 (2) ◽  
pp. 022057
Author(s):  
S V Sarkisov ◽  
S Z El-Salim ◽  
A V Bondarev ◽  
A N Korpusov ◽  
P A Putilin
Keyword(s):  
Wavelet Decomposition ◽  
Nonlinear Dynamical Systems ◽  
Hermite Polynomials ◽  
Nonlinear Dynamical ◽  
Self Similar ◽  
Quality Of Approximation ◽  
Approximation Coefficients ◽  
Similar Basis ◽  
Decomposition Of Functions

Abstract The paper considers Hermite polynomials that act as a self-similar basis for the decomposition of functions in phase space. It is shown that the equations of behavior of nonlinear dynamical systems are simplified. It is also noted that the wavelet decomposition over Hermite polynomials reduces the number of approximation coefficients and improves the quality of approximation.

scholarly journals Direction-aware Feature-level Frequency Decomposition for Single Image Deraining

2021 ◽  
Author(s):  
Sen Deng ◽  
Yidan Feng ◽  
Mingqiang Wei ◽  
Haoran Xie ◽  
Yiping Chen ◽  
...  
Keyword(s):  
High Frequency ◽  
State Of The Art ◽  
Low Frequency ◽  
Communication Channels ◽  
Input Image ◽  
Experimental Results ◽  
Training Procedure ◽  
Single Image ◽  
Frequency Decomposition

We present a novel direction-aware feature-level frequency decomposition network for single image deraining. Compared with existing solutions, the proposed network has three compelling characteristics. First, unlike previous algorithms, we propose to perform frequency decomposition at feature-level instead of image-level, allowing both low-frequency maps containing structures and high-frequency maps containing details to be continuously refined during the training procedure. Second, we further establish communication channels between low-frequency maps and high-frequency maps to interactively capture structures from high-frequency maps and add them back to low-frequency maps and, simultaneously, extract details from low-frequency maps and send them back to high-frequency maps, thereby removing rain streaks while preserving more delicate features in the input image. Third, different from existing algorithms using convolutional filters consistent in all directions, we propose a direction-aware filter to capture the direction of rain streaks in order to more effectively and thoroughly purge the input images of rain streaks. We extensively evaluate the proposed approach in three representative datasets and experimental results corroborate our approach consistently outperforms state-of-the-art deraining algorithms.

Learning a multi-level guided residual network for single image deraining

2019 ◽  
Vol 78 ◽  
pp. 206-215 ◽  
Author(s):  
Cong Wang ◽  
Man Zhang ◽  
Zhixun Su ◽  
Yutong Wu ◽  
Guangle Yao ◽  
...  
Keyword(s):  
Single Image ◽  
Residual Network ◽  
Multi Level
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