Nighttime image dehazing based on Retinex and dark channel prior using Taylor series expansion

Aiming at the color distortion of the restored image in the sky region, we propose an image dehazing algorithm based on double priors constraint. Firstly, we divided the haze image into sky and non-sky regions. Then the Color-lines prior and dark channel prior are used for estimating the transmission of sky and non-sky regions respectively. After introducing color-lines prior to correct sky regions restored by the dark channel prior, we get an accurate transmission. Finally, the local media mean value and standard deviation are used to refine the transmission to obtain the dehazing image. Experimental results show that the algorithm has obvious advantages in the recovery of the sky area.

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Restoration and Enhancement of Underwater Images Based on Bright Channel Prior

Mathematical Problems in Engineering ◽

10.1155/2016/3141478 ◽

2016 ◽

Vol 2016 ◽

pp. 1-15 ◽

Cited By ~ 9

Author(s):

Yakun Gao ◽

Haibin Li ◽

Shuhuan Wen

Keyword(s):

Histogram Equalization ◽

New Method ◽

Dark Channel Prior ◽

Image Dehazing ◽

Underwater Environment ◽

Dark Channel ◽

Bright Channel Prior ◽

Channel Image ◽

Atmospheric Light

This paper proposed a new method of underwater images restoration and enhancement which was inspired by the dark channel prior in image dehazing field. Firstly, we proposed the bright channel prior of underwater environment. By estimating and rectifying the bright channel image, estimating the atmospheric light, and estimating and refining the transmittance image, eventually underwater images were restored. Secondly, in order to rectify the color distortion, the restoration images were equalized by using the deduced histogram equalization. The experiment results showed that the proposed method could enhance the quality of underwater images effectively.

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Soliton dynamics in a formamide stack using a Taylor-series expansion for the potential surface: The Hartree-Fock potential

Physical Review A ◽

10.1103/physreva.38.939 ◽

1988 ◽

Vol 38 (2) ◽

pp. 939-952 ◽

Cited By ~ 12

Author(s):

Wolfgang Förner

Keyword(s):

Series Expansion ◽

Taylor Series ◽

Potential Surface ◽

Taylor Series Expansion ◽

Hartree Fock ◽

Soliton Dynamics

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Genetic Algorithm-based Dark Channel Prior Parameters Selection for Single Underwater Image Dehazing

2020 IEEE REGION 10 CONFERENCE (TENCON) ◽

10.1109/tencon50793.2020.9293849 ◽

2020 ◽

Author(s):

Vincent Jan D. Almero ◽

Ronnie S. Concepcion ◽

Jonnel D. Alejandrino ◽

Argel A. Bandala ◽

Jason L. Espanola ◽

...

Keyword(s):

Genetic Algorithm ◽

Dark Channel Prior ◽

Image Dehazing ◽

Underwater Image ◽

Parameters Selection ◽

Dark Channel ◽

Selection For

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Full-Scale Bounds Estimation for the Nonlinear Transient Heat Transfer Problems with Interval Uncertainties

International Journal of Computational Methods ◽

10.1142/s0219876221500262 ◽

2021 ◽

pp. 2150026

Author(s):

Ruifei Peng ◽

Haitian Yang ◽

Yanni Xue

Keyword(s):

Heat Transfer ◽

Series Expansion ◽

Taylor Series ◽

Taylor Series Expansion ◽

Transient Heat Transfer ◽

High Fidelity ◽

Interval Scale ◽

Nonlinear Transient ◽

Transfer Problems ◽

Derivatives Of

A package solution is presented for the full-scale bounds estimation of temperature in the nonlinear transient heat transfer problems with small or large uncertainties. When the interval scale is relatively small, an efficient Taylor series expansion-based bounds estimation of temperature is stressed on the acquirement of first and second-order derivatives of temperature with high fidelity. When the interval scale is relatively large, an optimization-based approach in conjunction with a dimension-adaptive sparse grid (DSG) surrogate is developed for the bounds estimation of temperature, and the heavy computational burden of repeated deterministic solutions of nonlinear transient heat transfer problems can be efficiently alleviated by the DSG surrogate. A temporally piecewise adaptive algorithm with high fidelity is employed to gain the deterministic solution of temperature, and is further developed for recursive adaptive computing of the first and second-order derivatives of temperature. Therefore, the implementation of Taylor series expansion and the construction of DSG surrogate are underpinned by a reliable numerical platform. The parallelization is utilized for the construction of DSG surrogate for further acceleration. The accuracy and efficiency of the proposed approaches are demonstrated by two numerical examples.

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Weighted Measurement Fusion Particle Filter for Nonlinear Systems with Correlated Noises

Sensors ◽

10.3390/s18103242 ◽

2018 ◽

Vol 18 (10) ◽

pp. 3242 ◽

Cited By ~ 1

Author(s):

Ke Wei Zhang ◽

Gang Hao ◽

Shu Li Sun

Keyword(s):

Nonlinear Systems ◽

Particle Filter ◽

Series Expansion ◽

Taylor Series ◽

Asymptotic Optimality ◽

Taylor Series Expansion ◽

Full Rank ◽

Expansion Method ◽

Least Square ◽

Correlated Noises

The multi-sensor information fusion particle filter (PF) has been put forward for nonlinear systems with correlated noises. The proposed algorithm uses the Taylor series expansion method, which makes the nonlinear measurement functions have a linear relationship by the intermediary function. A weighted measurement fusion PF (WMF-PF) was put forward for systems with correlated noises by applying the full rank decomposition and the weighted least square theory. Compared with the augmented optimal centralized fusion particle filter (CF-PF), it could greatly reduce the amount of calculation. Moreover, it showed asymptotic optimality as the Taylor series expansion increased. The simulation examples illustrate the effectiveness and correctness of the proposed algorithm.

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