Evaluation of underwater image enhancement algorithms based on Retinex and its implementation on embedded systems

Aguirre-Castro, O.A.; García-Guerrero, E. E.; López-Bonilla, Oscar Roberto; Tlelo-Cuautle, E.; López-Mancilla, D.; Cárdenas-Valdez, José Ricardo; Tiznado, Jesús Everardo Olguín; Inzunza-González, Everardo

doi:10.1016/j.neucom.2022.04.074

Cited by 33 publications

(12 citation statements)

References 40 publications

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“…The ML algorithms proposed in this research study could be implemented in highperformance embedded systems or edge computing devices as verified in previous studies [59,89]. These act as the central control system, which is in charge of communicating with the BCI to acquire EEG signals.…”

Section: Proposed Usage Scenariomentioning

confidence: 71%

Evaluation of Machine Learning Algorithms for Classification of EEG Signals

et al. 2022

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In brain–computer interfaces (BCIs), it is crucial to process brain signals to improve the accuracy of the classification of motor movements. Machine learning (ML) algorithms such as artificial neural networks (ANNs), linear discriminant analysis (LDA), decision tree (D.T.), K-nearest neighbor (KNN), naive Bayes (N.B.), and support vector machine (SVM) have made significant progress in classification issues. This paper aims to present a signal processing analysis of electroencephalographic (EEG) signals among different feature extraction techniques to train selected classification algorithms to classify signals related to motor movements. The motor movements considered are related to the left hand, right hand, both fists, feet, and relaxation, making this a multiclass problem. In this study, nine ML algorithms were trained with a dataset created by the feature extraction of EEG signals.The EEG signals of 30 Physionet subjects were used to create a dataset related to movement. We used electrodes C3, C1, CZ, C2, and C4 according to the standard 10-10 placement. Then, we extracted the epochs of the EEG signals and applied tone, amplitude levels, and statistical techniques to obtain the set of features. LabVIEW™2015 version custom applications were used for reading the EEG signals; for channel selection, noise filtering, band selection, and feature extraction operations; and for creating the dataset. MATLAB 2021a was used for training, testing, and evaluating the performance metrics of the ML algorithms. In this study, the model of Medium-ANN achieved the best performance, with an AUC average of 0.9998, Cohen’s Kappa coefficient of 0.9552, a Matthews correlation coefficient of 0.9819, and a loss of 0.0147. These findings suggest the applicability of our approach to different scenarios, such as implementing robotic prostheses, where the use of superficial features is an acceptable option when resources are limited, as in embedded systems or edge computing devices.

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Section: Proposed Usage Scenariomentioning

confidence: 71%

Evaluation of Machine Learning Algorithms for Classification of EEG Signals

et al. 2022

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“…Instead, they enhance visual perception by adjusting image contrast and emphasizing image features. In recent years, a range of effective underwater image enhancement methods have been proposed, including white balance algorithms, Retinex algorithms [1][2][3] , and color correction algorithms [4][5][6] . In recent years, many researchers have proposed improved underwater image enhancement methods based on this algorithm.…”

Section: Related Workmentioning

confidence: 99%

An illumination adaptive underwater image enhancement method

Zheng,

Miao,

Zhang

et al. 2024

International Conference on Algorithm, Imaging Processing, and Machine Vision (AIPMV 2023)

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In underwater imagery, issues such as non-uniform illumination, blurriness, and low contrast are prevalent, significantly impacting the quality of captured images. In recent years, numerous researchers have delved into underwater image processing. Due to the intricacies of underwater environments, low-light images have different requirements compared to well-illuminated ones. However, existing algorithms often struggle to address the non-uniform illumination issues stemming from various lighting conditions in underwater settings. They also lack the capability to adaptively enhance underwater images with varying brightness. To tackle these challenges, we propose an adaptive illumination enhancement method for underwater images. This algorithm offers the capability to adaptively enhance underwater images suffering from detail blurriness based on their original brightness. Furthermore, it dynamically adjusts the parameters of the gamma function using the image's illumination component to augment color contrast. Experimental results demonstrate that our approach outperforms other algorithms, as evidenced by superior scores in UIQM metric. It effectively addresses edge blurriness and non-uniform illumination issues prevalent in underwater images captured under varying lighting conditions.

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“…( , ) 1 F x y dxdy (5) The scale parameter is C, and the parameters that are selected have a direct impact on how well an image is enhanced [8] . MSR, which preserves small details in the image while boosting contrast, is a weighted average of three SSR scales [9] .…”

Section: Msrmentioning

confidence: 99%

Low-light road image enhancement based on optimized MSR

Lan,

Huang,

Huang

2024

Third International Conference on Advanced Manufacturing Technology and Electronic Information (AMTEI 2023)

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Utilizing machine vision to acquire road environment information is a crucial factor influencing the performance of advanced driver assistance systems, but under low-light conditions, obtaining high-quality road information directly through vision becomes challenging, necessitating image enhancement. Due to uneven illumination in low-light conditions, existing enhancement methods often lead to issues such as glare and blurred details in bright areas. In this paper, we propose an optimized Multi-Scale Retinex (MSR) image enhancement algorithm.Firstly, RGB images are converted to the YUV format, and the MSR algorithm is applied to enhance the Y channel. The local background brightness is then incorporated into the Just Noticeable Difference (JND) model to establish a non-linear relationship model between background brightness and adjustment factors. This enables adaptive adjustment of image enhancement intensity. Subsequently, a non-linear bilateral filtering function is applied to smooth the adjusted image, followed by Contrast Limited Adaptive Histogram Equalization (CLAHE) to enhance its contrast. Finally, the resulting image is fused with the original image in a 1:1 ratio.Experimental results indicate that, compared to the combination of MSR and histogram equalization, the proposed method achieves a 4.5% increase in standard deviation, a 7% increase in information entropy, and a 25% improvement in peak signal-to-noise ratio.

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Evaluation of underwater image enhancement algorithms based on Retinex and its implementation on embedded systems

Cited by 33 publications

References 40 publications

Evaluation of Machine Learning Algorithms for Classification of EEG Signals

Evaluation of Machine Learning Algorithms for Classification of EEG Signals

An illumination adaptive underwater image enhancement method

Low-light road image enhancement based on optimized MSR

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