Nighttime Defogging Using High-Low Frequency Decomposition and Grayscale-Color Networks

2020 ◽  
pp. 473-488
Author(s):  
Wending Yan ◽  
Robby T. Tan ◽  
Dengxin Dai
Keyword(s):  
Low Frequency ◽  
Frequency Decomposition

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.

Nonstationary local time-frequency transform

Geophysics ◽  
2021 ◽  
Vol 86 (3) ◽  
pp. V245-V254
Author(s):  
Yangkang Chen
Keyword(s):  
Local Time ◽  
Seismic Data ◽  
Low Frequency ◽  
Data Sets ◽  
Frequency Spectra ◽  
Time Frequency ◽  
Data Registration ◽  
Frequency Decomposition ◽  
Temporal Smoothing ◽  
The Time Domain

Time-frequency analysis is a fundamental approach to many seismic problems. Time-frequency decomposition transforms input seismic data from the time domain to the time-frequency domain, offering a new dimension to probe the hidden information inside the data. Considering the nonstationary nature of seismic data, time-frequency spectra can be obtained by applying a local time-frequency transform (LTFT) method that matches the input data by fitting the Fourier basis with nonstationary Fourier coefficients in the shaping regularization framework. The key part of LTFT is the temporal smoother with a fixed smoothing radius that guarantees the stability of the nonstationary least-squares fitting. We have developed a new LTFT method to handle the nonstationarity in all time, frequency, and space ( x and y) directions of the input seismic data by extending fixed-radius temporal smoothing to nonstationary smoothing with a variable radius in all physical dimensions. The resulting time-frequency transform is referred to as the nonstationary LTFT method, which could significantly increase the resolution and antinoise ability of time-frequency transformation. There are two meanings of nonstationarity, i.e., coping with the nonstationarity in the data by LTFT and dealing with the nonstationarity in the model by nonstationary smoothing. We evaluate the performance of our nonstationary LTFT method in several standard seismic applications via synthetic and field data sets, e.g., arrival picking, quality factor estimation, low-frequency shadow detection, channel detection, and multicomponent data registration, and we benchmark the results with the traditional stationary LTFT method.

scholarly journals Pullback-Forward Dynamics for Damped Schrödinger Equations with Time-Dependent Forcing

2018 ◽  
Vol 2018 ◽  
pp. 1-14
Author(s):  
Lianbing She ◽  
Yangrong Li ◽  
Renhai Wang
Keyword(s):  
Schrödinger Equation ◽  
Schrodinger Equation ◽  
A Priori ◽  
Time Derivative ◽  
Low Frequency ◽  
Time Dependent ◽  
Sobolev Embedding ◽  
Pullback Attractor ◽  
Low Frequencies ◽  
Frequency Decomposition

This paper deals with pullback dynamics for the weakly damped Schrödinger equation with time-dependent forcing. An increasing, bounded, and pullback absorbing set is obtained if the forcing and its time-derivative are backward uniformly integrable. Also, we obtain the forward absorption, which is only used to deduce the backward compact-decay decomposition according to high and low frequencies. Based on a new existence theorem of a backward compact pullback attractor, we show that the nonautonomous Schrödinger equation has a pullback attractor which is compact in the past. The method of energy, high-low frequency decomposition, Sobolev embedding, and interpolation are quite involved in calculating a priori pullback or forward bound.

scholarly journals POD and Fourier analyses of a fluid-structure-acoustic interaction problem related to interior car noise

2017 ◽  
Vol 18 (2) ◽  
pp. 201
Author(s):  
Éric Gaudard ◽  
Philippe Druault ◽  
Régis Marchiano ◽  
François Van Herpe
Keyword(s):  
Pressure Field ◽  
Acoustic Radiation ◽  
Numerical Study ◽  
Low Frequency ◽  
Wall Pressure ◽  
Aerodynamic Noise ◽  
Frequency Noise ◽  
Post Processing ◽  
Fourier Decomposition ◽  
Frequency Decomposition

In order to approach a flow configuration revealing the aerodynamic noise contribution in the interior of road vehicles due to the A-pillar vortex, a numerical simulation of a Forward Facing Step (FFS) coupled with a vibrating structure is performed. This numerical study is based on a weak coupling of three solvers to compute (i) the flow field in interaction with the FFS, (ii) the vibration of the structure and (iii) the acoustic radiation in the open cavity. The purpose of this work is then to evaluate the ability of two different post-processing methods: Proper Orthogonal Decomposition and Fourier Decomposition to identify the origin of the noise radiated into a cavity surrounded by an unsteady flow. Fourier and POD decompositions are then successively performed to extract the part of the aeroacoustic wall pressure field impacting the upper part of an upward step mainly related to the radiated acoustic pressure in the cavity. It is observed that the acoustic part, extracted from the wavenumber frequency decomposition (Fourier analysis) of the wall pressure field generates a non-negligible part of the interior cavity noise. However, this contribution is of several orders smaller than the one related to the aerodynamic part of the pressure field. Moreover, it is shown that the most energetic part of the pressure field (POD analysis) is due to the shear flapping motion and mainly contributes to the low-frequency noise in the cavity. Such post-processing results are of particular interest for future analyzes related to the noise radiated inside a car.

Feature Extraction of Fault Engine Audio Signal Based on Wavelet Packet Transform

2012 ◽  
Vol 546-547 ◽  
pp. 675-679
Author(s):  
Gao Huan Xu ◽  
Jun Xiang Ye
Keyword(s):  
Feature Extraction ◽  
Wavelet Packet ◽  
Low Frequency ◽  
Audio Signal ◽  
Wavelet Packet Transform ◽  
Wavelet Packet Decomposition ◽  
Energy Characteristics ◽  
Signal Noise ◽  
Frequency Decomposition ◽  
Machine Parts

Due to the different structure of the machine parts, machine vibrations sent audio signal have different frequency. The early defect, audio signal can be analyzed well by wavelet packet transform. After wavelet packet decomposition and reconstruction, Audio signal noise reduced. And then through high and low frequency decomposition, we can constitute the energy characteristics. The experiment shows: the extracted features have good structure.

scholarly journals A Frequency Decomposition-Based Hybrid Forecasting Algorithm for Short-Term Reactive Power

Energies ◽  
2021 ◽  
Vol 14 (20) ◽  
pp. 6606
Author(s):  
Jiabao Du ◽  
Changxi Yue ◽  
Ying Shi ◽  
Jicheng Yu ◽  
Fan Sun ◽  
...  
Keyword(s):  
High Frequency ◽  
Reactive Power ◽  
Short Term Memory ◽  
Low Frequency ◽  
Ensemble Empirical Mode Decomposition ◽  
Short Term ◽  
Prediction Ability ◽  
Frequency Decomposition ◽  
Mode Decomposition ◽  
Speed Up

This paper proposes a new frequency decomposition-based hybrid reactive power forecasting algorithm, EEMD-LSTM-RFR (ELR), which adopts a strategy of frequency decomposition prediction after ensemble empirical mode decomposition and then data reconstruction to improve the prediction ability of reactive power. This decomposition process can compress the high frequency of reactive power and benefits the following separate forecasting. Long short-term memory is proposed for the high-frequency feature of reactive power to deal with the forecasting difficulty caused by strong signal disturbance and randomness. In contrast, random forest regression is applied to the low-frequency part in order to speed up the forecasting. Four classical algorithms and four hybrid algorithms based on different signal decompositions are compared with the proposed algorithm, and the results show that the proposed algorithm outperforms those algorithms. The predicting index RMSE decreases to 0.687, while the fitting degree R2 gradually approaches 1 with a step-by-step superposition of high-frequency signals, indicating that the proposed decomposition-predicting reconstruction strategy is effective.

scholarly journals APPLICATION OF TIME-FREQUENCY DECOMPOSITION METHOD FOR GAS RESERVOIR IDENTIFICATION IN THE SERGIPE - ALAGOAS BASIN

2018 ◽  
Vol 36 (2) ◽  
pp. 189
Author(s):  
Alexandre De Jesus Pinho ◽  
Milton José Porsani
Keyword(s):  
Spectral Decomposition ◽  
Reservoir Characterization ◽  
Low Frequency ◽  
Seismic Attributes ◽  
Entropy Method ◽  
Hydrocarbon Exploration ◽  
Maximal Entropy ◽  
Time Frequency ◽  
High Attenuation ◽  
Frequency Decomposition

ABSTRACT. The sedimentary basin of Sergipe-Alagoas, located on the Brazilian east bank, presents one of the most complete stratigraphic sections of the Brazilian continental margin. Hydrocarbon exploration activities began more than 50 years ago. The recent discoveries of hydrocarbons (gas and oil of high API grade) in turbiditic reservoirs of deep waters have further awakened the exploratory interest of the basin. Problems related to the processing and interpretation of seismic data have always received great attention from the scientific community. Currently, the use of time-frequency decomposition methods of the seismic signal is of great interest. Spectral decomposition has been widely used in reservoir characterization, such as determination of layer thickness, stratigraphic visualization with seismic attributes and identification of low frequency anomalies associated with the presence of gas. The mechanism causing these anomalies is not yet well known, but they are often attributed to the high attenuation of gas filled reservoirs. The approach used for spectral decomposition combines the maximal entropy method and the Wigner-Ville distribution, based on the idea of the Burg method that uses the prediction error operator to extend the Wigner-Ville kernel sequences by applying, then, the Fourier transform to each extended sequence of the Kernel, thus allowing to obtain the Wigner-Ville distribution of maximum Entropy.Keywords: Sergipe-Alagoas Basin, Wigner-Ville distribution, Maximum Entropy, spectral decomposition, seismic attributes, low frequency anomaly.RESUMO. A bacia sedimentar de Sergipe-Alagoas, localizada na margem leste brasileira, apresenta uma das mais completas seções estratigráficas da margem continental brasileira. As atividades exploratórias de hidrocarbonetos foram iniciadas há mais de 50 anos. As recentes descobertas de hidrocarbonetos (gás e óleo de elevado grau API) em reservatórios turbidíticos de águas profundas despertaram ainda mais o interesse exploratório da bacia. Os problemas relacionados ao processamento e `a interpretação de dados sísmicos sempre receberam grande atenção da comunidade científica. Atualmente, desperta grande interesse o uso de métodos de decomposição tempo-frequência do sinal sísmico. A decomposição espectral tem sido bastante utilizada na caracterização de reservatório, como estimativa de espessura de camada, visualização estratigráfica com atributos sísmicos e identificação de anomalias de baixa frequência que podem estar associadas `a presença de gás. O mecanismo causador dessas anomalias ainda não é perfeitamente conhecido, mas é frequentemente atribuído a atenuação das altas frequências nos reservatórios preenchidos com gás. A abordagem utilizada para a decomposição espectral combina o método de máxima entropia e a distribuição de Wigner-Ville, com base na ideia do Método de Burg que usa o operador de erro de predição para estender as sequências do kernel de Wigner-Ville aplicando a transformada de Fourier para cada sequência estendida do kernel, permitindo assim, obter a distribuição Wigner-Ville de máxima entropia.Palavras-chave: Bacia Sergipe-Alagoas, distribuição Wigner-Ville, Máxima Entropia, decomposição espectral, atributos sísmicos, anomalia de baixa frequência. Universidade Federal

scholarly journals Global well-posedness for the generalized Navier-Stokes-Coriolis equations with highly oscillating initial data

2021 ◽  
Author(s):  
Xiaochun Sun ◽  
Mixiu Liu ◽  
Jihong Zhang
Keyword(s):  
Cauchy Problem ◽  
Fixed Point ◽  
Besov Spaces ◽  
Initial Velocity ◽  
Low Frequency ◽  
Navier Stokes ◽  
Banach Fixed Point Theorem ◽  
Well Posedness ◽  
Smoothing Effect ◽  
Frequency Decomposition

We study the small initial date Cauchy problem for the generalized incompressible Navier-Stokes-Coriolis equations in critical hybrid-Besov space $\dot{\mathscr{B}}_{2,\, p}^{\frac{5}{2}-2\alpha, \frac{3}{p}-2\alpha+1}(\mathbb{R}^3)$ with $1/2<\alpha<2$ and $2\leq p\leq 4$. We prove that hybrid-Besov spaces norm of a class of highly osillating initial velocity can be arbitrarily small. and we prove the estimation of highly frequency $L^p$ smoothing effect for generalized Stokes-Coriolis semigroup with $1\leq p\leq\infty$, At the same time, we prove space-time norm estimation of hybrid-Besov spaces for Stokes-Coriolis semigroup. From this result we deduce bilinear estimation in our work space. Our method relies upon Bony’s high and low frequency decomposition technology and Banach fixed point theorem.

scholarly journals GLOBAL EXPONENTIAL STABILITY OF CLASSICAL SOLUTIONS TO THE HYDRODYNAMIC MODEL FOR SEMICONDUCTORS

2007 ◽  
Vol 17 (10) ◽  
pp. 1507-1530 ◽  
Author(s):  
DAOYUAN FANG ◽  
JIANG XU ◽  
TING ZHANG
Keyword(s):  
Exponential Stability ◽  
Hydrodynamic Model ◽  
Global Exponential Stability ◽  
Local Existence ◽  
Low Frequency ◽  
Classical Solutions ◽  
Frequency Decomposition ◽  
3D Space ◽  
The Cauchy Problem ◽  
2D And 3D

In this paper, the global well-posedness and stability of classical solutions to the multidimensional hydrodynamic model for semiconductors on the framework of Besov space are considered. We weaken the regularity requirement of the initial data, and improve some known results in Sobolev space. The local existence of classical solutions to the Cauchy problem is obtained by the regularized means and compactness argument. Using the high- and low-frequency decomposition method, we prove the global exponential stability of classical solutions (close to equilibrium). Furthermore, it is also shown that the vorticity decays to zero exponentially in the 2D and 3D space. The main analytic tools are the Littlewood–Paley decomposition and Bony's para-product formula.

Sign in / Sign up

Export Citation Format

Share Document