Underwater captured images often suffer from color cast and low visibility due to light is scattered and absorbed while it traveling in water. In this paper, we proposed a novel method of color correction and Bi-interval contrast enhancement to improve the quality of underwater images. Firstly, a simple and effective color correction method based on sub-interval linear transformation is employed to address color distortion. Then, a Gaussian low-pass filter is applied to the L channel to decompose the low- and high-frequency components. Finally, the low- and high-frequency components are enhanced by Bi-interval histogram based on optimal equalization threshold strategy and S-shaped function to enhancement image contrast and highlight image details. Inspired by the multi-scale fusion, we employed a simple linear fusion to integrate the enhanced high- and low-frequency components. Comparison with state-of-the-art methods show that the proposed method outputs high-quality underwater images with qualitative and quantitative evaluation well.

Enhancing underwater image via color correction and Bi-interval contrast enhancement

Underwater images suffer from color cast and low visibility caused by the medium scattering and absorption, which will reduce the use of valuable information from the image. In this paper, we propose a novel method which includes four stages of pixel intensity center regionalization, global equalization of histogram, local equalization of histogram and multi-scale fusion. Additionally, this method uses a pixel intensity center regionalization strategy to perform centralization of the image histogram on the overall image. Global equalization of histogram is employed to correct color of the image according to the characteristics of each channel. Local equalization of dual-interval histogram based on average of peak and mean values is used to improve contrast of the image according to the characteristics of each channel. Dual-image multi-scale fusion to integrate the contrast, saliency and exposure weight maps of the color corrected and contrast enhanced images. Experiments on variety types of degraded underwater images show that the proposed method produces better output results in both qualitative and quantitative analysis, thus, the proposed method outperforms other state-of-the-art methods.

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Underwater Image Enhancement Based on Global and Local Equalization of Histogram and Dual-Image Multi-Scale Fusion

Color correction and adaptive contrast enhancement for underwater image enhancement

Retinex-inspired color correction and detail preserved fusion for underwater image enhancement

In the existing electroencephalogram (EEG) signals peak classification research, the existing models, such as Dumpala, Acir, Liu, and Dingle peak models, employ different set of features. However, all these models may not be able to offer good performance for various applications and it is found to be problem dependent. Therefore, the objective of this study is to combine all the associated features from the existing models before selecting the best combination of features. A new optimization algorithm, namely as angle modulated simulated Kalman filter (AMSKF) will be employed as feature selector. Also, the neural network random weight method is utilized in the proposed AMSKF technique as a classifier. In the conducted experiment, 11,781 samples of peak candidate are employed in this study for the validation purpose. The samples are collected from three different peak event-related EEG signals of 30 healthy subjects; (1) single eye blink, (2) double eye blink, and (3) eye movement signals. The experimental results have shown that the proposed AMSKF feature selector is able to find the best combination of features and performs at par with the existing related studies of epileptic EEG events classification.

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Feature selection using angle modulated simulated Kalman filter for peak classification of EEG signals.

Natural-based underwater image color enhancement through fusion of swarm-intelligence algorithm

Inspired by the estimation capability of Kalman filter, we have recently introduced a novel estimation-based optimization algorithm called simulated Kalman filter (SKF). Every agent in SKF is regarded as a Kalman filter. Based on the mechanism of Kalman filtering and measurement process, every agent estimates the global minimum/maximum. Measurement, which is required in Kalman filtering, is mathematically modelled and simulated. Agents communicate among them to update and improve the solution during the search process. However, the SKF is only capable to solve continuous numerical optimization problem. In order to solve discrete optimization problems, the SKF algorithm is combined with an angle modulated approach. The performance of the proposed angle modulated SKF (AMSKF) is compared against two other discrete population-based optimization algorithms, namely, binary particle swarm optimization (BPSO) and binary gravitational search algorithm (BGSA). A set of traveling salesman problems are used to evaluate the performance of the proposed AMSKF. Based on the analysis of experimental results, we found that the proposed AMSKF is as competitive as BGSA but the BPSO is superior to the both AMSKF and BGSA.

/pdf/angle-modulated-simulated-kalman-filter-algorithm-for-4mivh724pq.pdf

Angle Modulated Simulated Kalman Filter Algorithm for Combinatorial Optimization Problems

Inspired by the estimation capability of Kalman filter, we have recently introduced a novel population-based
optimization algorithm called simulated Kalman filter (SKF). Every agent in SKF is regarded as a Kalman filter. Based on the mechanism of Kalman filtering, which includes prediction, measurement, and estimation, the global minimum/maximum can be estimated. Measurement process, which is required in Kalman filtering, is mathematically modelled and simulated. Agents communicate among them to update and improve the solution during the search process.Inspired by the bird flocking, particle swarm optimization (PSO) has been introduced in 1994. In PSO, a swarm of agent search the global minimum/maximum by velocity and position updates, which are influenced by current position of agent,current position of agent, personal best, and global best of the swarm. In this research, SKF and PSO are hybridized in such
a way that PSO is employed as prediction operator in SKF. The performance of the proposed hybrid SKF-PSO algorithm
(SKF-PSO) is compared against SKF and PSO using CEC2014 benchmark dataset for continuous numerical optimization
problems. Based on the analysis of experimental results, we found that the proposed hybrid SKF-PSO is superior than both SKF and PSO algorithm

/pdf/a-new-hybrid-simulated-kalman-filter-and-particle-swarm-3ab4qbbxk3.pdf

A New Hybrid Simulated Kalman Filter and Particle Swarm Optimization for Continuous Numerical Optimization Problems

In this paper, a population-based metaheuristic optimization algorithm named Opposition-based Simulated Kalman Filter (OBSKF) is proposed as an adaptive beamforming algorithm. This algorithm is based on Simulated Kalman Filter (SKF), which is a newly introduced optimization algorithm inspired by Kalman filtering. Opposition-based learning is employed in SKF to improve the performance of SKF in adaptive beamforming. Adaptive beamforming algorithm based on OBSKF is compared with SKF and the existing Adaptive Mutated Boolean Particle Swarm Optimization (AMBPSO). Experimental results show that adaptive beamforming algorithm based on OBSKF is better than SKF and AMBPSO.

An Opposition-based Simulated Kalman Filter algorithm for adaptive beamforming

Blue–green colour cast effect and low contrast are common problems suffered by deep underwater images. This paper introduces a new method which consists of two major steps: red channel correction b...

Kamil Zakwan Mohd Azmi

Papers

Natural-based underwater image color enhancement through fusion of swarm-intelligence algorithm

Angle Modulated Simulated Kalman Filter Algorithm for Combinatorial Optimization Problems

A New Hybrid Simulated Kalman Filter and Particle Swarm Optimization for Continuous Numerical Optimization Problems

An Opposition-based Simulated Kalman Filter algorithm for adaptive beamforming

Deep underwater image enhancement through colour cast removal and optimization algorithm