A Novel Underwater Image Enhancement Approach with Wavelet Transform Supported by Differential Evolution Algorithm

Güraksın, Gür Emre; Deperlioğlu, Ömer; Köse, Utku

doi:10.1007/978-3-319-96002-9_11

Cited by 16 publications

(7 citation statements)

References 37 publications

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“…Guraksin et al 30 enhanced the degraded image that used the discrete wavelet transform along with a differential evolution approach. First, the contrast was improved, and then the homomorphic filter was used, resulting in enhanced brightness and contrast.…”

Section: Non-learning-based Methodsmentioning

confidence: 99%

Systematic review and analysis on underwater image enhancement methods, datasets, and evaluation metrics

Verma

Kumar

2022

J. Electron. Imag.

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valuable resources that exist beneath Earth's surface. Underwater exploration requires enhanced images that are obtained using enhancement methods. So, it is important that underwater image enhancement (UIE) methods work well in terms of performance and accuracy. As a result, research in UIE has increased in the past few years. An extensive survey is conducted on existing UIE methods along with their broad classification, underwater datasets, and evaluation metrics, respectively. The experimental analysis is conducted to compare the existing UIE methods in terms of qualitative and quantitative evaluation metrics. The real-world applications and future scope of existing enhancement methods are highlighted and discussed.

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Section: Non-learning-based Methodsmentioning

confidence: 99%

Systematic review and analysis on underwater image enhancement methods, datasets, and evaluation metrics

Verma

Kumar

2022

J. Electron. Imag.

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“…Guraksin et al [121] presented a method for underwater images that was depended on a wavelet transform algorithm and the differential evolution algorithm. First, contrast adjustment on underwater images.…”

Section: Contrast-based Image Enhancementmentioning

confidence: 99%

Underwater Image Restoration and Enhancement: A Comprehensive Review of Recent Trends, Challenges, and Applications

Alsakar¹,

Sakr²,

El–Sappagh³

et al. 2023

Preprint

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In recent years, underwater exploration for deep-sea resource utilization and development has a considerable interest. In an underwater environment, the obtained images and videos undergo several types of quality degradation resulting from light absorption and scattering, low contrast, color deviation, blurred details, and nonuniform illumination. Therefore, the restoration and enhancement of degraded images and videos are critical. Numerous techniques of image processing, pattern recognition and computer vision have been proposed for image restoration and enhancement, but many challenges remain. This survey presents a comparison of the most prominent approaches in underwater image processing and analysis. It also discusses an overview of the underwater environment with a broad classification into enhancement and restoration techniques and introduces the main underwater image degradation reasons in addition to the underwater image model. The existing underwater image analysis techniques, methods, datasets, and evaluation metrics are presented in detail. Furthermore, the existing limitations are analyzed, which are classified into image-related and environment-related categories. In addition, the performance is validated on images from the UIEB dataset for qualitative, quantitative, and computational time assessment. Areas in which underwater images have recently been applied are briefly discussed. Finally, recommendations for future research are provided and the conclusion is presented.

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“…Maximum fitness is found in a population shared with all other groups to diversify the population. Multiple classifiers are used to find healthy and unhealthy cells Bhandari [ 242 ] Fast multilevel thresholding for segmentation of the color image Beta DE(BDE) BDE employs beta distribution and sets the value for the parameters scale factor and crossover during the search process of DE Bidgoliet al [ 245 ] Multilevel feature selection BGDE3 The selection of features is a binary optimization problem; here, the GDE3 algorithm's binary version is used to extract features Guraksin et al [ 246 ] Enhancement of underwater image DE with wavelet transform Firstly, the image's contrast was adjusted using a contrast adjustment function; then, brightness was normalized using the homomorphic filtering technique. R, G, and B components were separated, and the wavelet transform function was applied to find parameters within color components.…”

Section: Application Of De In Image Processing Problemsmentioning

confidence: 99%

Differential Evolution and Its Applications in Image Processing Problems: A Comprehensive Review

Chakraborty

Saha

Ezugwu

et al. 2022

Arch Computat Methods Eng

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Differential evolution (DE) is one of the highly acknowledged population-based optimization algorithms due to its simplicity, user-friendliness, resilience, and capacity to solve problems. DE has grown steadily since its beginnings due to its ability to solve various issues in academics and industry. Different mutation techniques and parameter choices influence DE's exploration and exploitation capabilities, motivating academics to continue working on DE. This survey aims to depict DE's recent developments concerning parameter adaptations, parameter settings and mutation strategies, hybridizations, and multi-objective variants in the last twelve years. It also summarizes the problems solved in image processing by DE and its variants.

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A Novel Underwater Image Enhancement Approach with Wavelet Transform Supported by Differential Evolution Algorithm

Cited by 16 publications

References 37 publications

Systematic review and analysis on underwater image enhancement methods, datasets, and evaluation metrics

Systematic review and analysis on underwater image enhancement methods, datasets, and evaluation metrics

Underwater Image Restoration and Enhancement: A Comprehensive Review of Recent Trends, Challenges, and Applications

Differential Evolution and Its Applications in Image Processing Problems: A Comprehensive Review

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