Sea Cucumber Image Dehazing Method by Fusion of Retinex and Dark Channel

Li, Zhenbo; Li, Guangyao; Niu, Bingshan; Peng, Fang-Le

doi:10.1016/j.ifacol.2018.08.098

Cited by 13 publications

(5 citation statements)

References 9 publications

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“…Such products contribute significantly to meeting people's dietary needs since they depend on animal protein for nutrition. Li et al [212] developed an effective and novel method for enhancing blurred, degraded, and color distorted underwater images. This method depended on the fusion of the prior dark channel and retinex.…”

Section: Sea Cucumber 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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Section: Sea Cucumber 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

View full text Add to dashboard Cite

show abstract

“…Li [64] proposed an underwater sea cucumber image enhancement method based on the fusion of the Retinex algorithm and dark channel prior algorithm. Using the evaluation indicators of the MSE, equivalent numbers of looks (ENL), information entropy (IE), and signal to noise ratio (SNR) values, this method has shown an advantageous effect regarding defogging and the enhancement of underwater sea cucumber images.…”

Section: Target Detection and Recognition Of Underwater Organismsmentioning

confidence: 99%

A Survey of Target Detection and Recognition Methods in Underwater Turbid Areas

et al. 2022

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Based on analysis of state-of-the-art research investigating target detection and recognition in turbid waters, and aiming to solve the problems encountered during target detection and the unique influences of turbidity areas, in this review, the main problem is divided into two areas: image degradation caused by the unique conditions of turbid water, and target recognition. Existing target recognition methods are divided into three modules: target detection based on deep learning methods, underwater image restoration and enhancement approaches, and underwater image processing methods based on polarization imaging technology and scattering. The relevant research results are analyzed in detail, and methods regarding image processing, target detection, and recognition in turbid water, and relevant datasets are summarized. The main scenarios in which underwater target detection and recognition technology are applied are listed, and the key problems that exist in the current technology are identified. Solutions and development directions are discussed. This work provides a reference for engineering tasks in underwater turbid areas and an outlook on the development of underwater intelligent sensing technology in the future.

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“…Galdran et al [22] demonstrated the defogging characteristics of the Retinex enhancement method by means of a formula in 2018. Li et al [30] fused dark channel prior information and the Retinex method to enhance the defogging of underwater sea cucumber images in the same year. After that, Zhou et al [2] and Zhang et al [3] proposed a Retinex-based laplacian pyramid method and an MSRCR multi-channel convolution method for image defogging, respectively.…”

Section: Related Workmentioning

confidence: 99%

Joint Dedusting and Enhancement of Top-Coal Caving Face via Single-Channel Retinex-Based Method with Frequency Domain Prior Information

Zhang

et al. 2021

Symmetry

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Affected by the uneven concentration of coal dust and low illumination, most of the images captured in the top-coal caving face have low definition, high haze and serious noise. In order to improve the visual effect of underground images captured in the top-coal caving face, a novel single-channel Retinex dedusting algorithm with frequency domain prior information is proposed to solve the problem that Retinex defogging algorithm cannot effectively defog and denoise, simultaneously, while preserving image details. Our work is inspired by the simple and intuitive observation that the low frequency component of dust-free image will be amplified in the symmetrical spectrum after adding dusts. A single-channel multiscale Retinex algorithm with color restoration (MSRCR) in YIQ space is proposed to restore the foggy approximate component in wavelet domain. After that the multiscale convolution enhancement and fast non-local means (FNLM) filter are used to minimize noise of detail components while retaining sufficient details. Finally, a dust-free image is reconstructed to the spatial domain and the color is restored by white balance. By comparing with the state-of-the-art image dedusting and defogging algorithms, the experimental results have shown that the proposed algorithm has higher contrast and visibility in both subjective and objective analysis while retaining sufficient details.

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Sea Cucumber Image Dehazing Method by Fusion of Retinex and Dark Channel

Cited by 13 publications

References 9 publications

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

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

A Survey of Target Detection and Recognition Methods in Underwater Turbid Areas

Joint Dedusting and Enhancement of Top-Coal Caving Face via Single-Channel Retinex-Based Method with Frequency Domain Prior Information

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