Deep retinex decomposition network for underwater image enhancement

Xu, Shuai; Zhang, Jian; Qin, Xin; Xiao, Yuchen; Qian, Jianjun; Bo, Liling; Zhang, Heng; Li, Hongran; Zhong, Zhaoman

doi:10.1016/j.compeleceng.2022.107822

Cited by 24 publications

(6 citation statements)

References 20 publications

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“…Xu et al 87 employed a retinex method in their learning method and presented a deep-retinexdecomposition network for enhancing underwater images. The authors focused on improving the color, contrast, and brightness and estimated the illumination using a CNN as the baseline model.…”

Section: 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: 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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“…The image becomes more in line with the human subjective perception [16]. Underwater image enhancement algorithms can be divided into 2 categories according to the domain of action: the former focuses on the improvement of image pixels, and the latter transforms the image into the frequency domain through the null-frequency transform, where some special properties of the image in the frequency domain are manipulated [17][18][19].…”

Section: Underwater Image Enhancementmentioning

confidence: 99%

Sea Cucumber Identification Fishing System Based on Hog Super Pixel Feature Extraction

Wang,

Liu,

Chen

et al. 2023

J. Phys.: Conf. Ser.

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Due to the refraction, reflection and absorption of light induced by seawater and suspended particles, the image recognition of underwater vehicle has the problem of low accuracy and difficult localization, and to solve this one problem, an improved LSTM classification algorithm (CH-LSTM) based on CLAHE and HOG is given. First, the improved CLAHE algorithm is used to enhance image by image noise removal and de-fogging, the problems of underwater image distortion, fine lines, and abrupt changes are solved in the aquaculture waters image. Then, the Histogram of Orientation Gradient (HOG) operator is used for feature extraction to describe the shape of sea cucumbers and generate feature vectors; finally, the LSTM algorithm is used to classify the feature vectors and avoid overfitting through memory gates, so the generalization ability of the model was enhanced. The identification and sorting experiments of underwater fishing sea cucumbers show that the proposed algorithm is superior, especially in the environment of poor water quality, and the identification and localization accuracy of sea cucumbers is improved compared with the traditional ground SVM and BP neural network algorithms, and the identification accuracy is above 95.28%.

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“…By including the unsettling V channel image component in the HSV color space, the component was converted to a reflection component using a DL network ( Jiang Z. et al, 2021 ). Owing to their significant worth, their Retinex and DL-based methods were applied in image dehazing and underwater image enhancement ( Xu et al, 2022 ; Shen et al, 2023 ).…”

Section: Introductionmentioning

confidence: 99%

DCENet-based low-light image enhancement improved by spiking encoding and convLSTM

Wang,

Zhang

et al. 2024

Front. Neurosci.

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The direct utilization of low-light images hinders downstream visual tasks. Traditional low-light image enhancement (LLIE) methods, such as Retinex-based networks, require image pairs. A spiking-coding methodology called intensity-to-latency has been used to gradually acquire the structural characteristics of an image. convLSTM has been used to connect the features. This study introduces a simplified DCENet to achieve unsupervised LLIE as well as the spiking coding mode of a spiking neural network. It also applies the comprehensive coding features of convLSTM to improve the subjective and objective effects of LLIE. In the ablation experiment for the proposed structure, the convLSTM structure was replaced by a convolutional neural network, and the classical CBAM attention was introduced for comparison. Five objective evaluation metrics were compared with nine LLIE methods that currently exhibit strong comprehensive performance, with PSNR, SSIM, MSE, UQI, and VIFP exceeding the second place at 4.4% (0.8%), 3.9% (17.2%), 0% (15%), 0.1% (0.2%), and 4.3% (0.9%) on the LOL and SCIE datasets. Further experiments of the user study in five non-reference datasets were conducted to subjectively evaluate the effects depicted in the images. These experiments verified the remarkable performance of the proposed method.

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Deep retinex decomposition network for underwater image enhancement

Cited by 24 publications

References 20 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

Sea Cucumber Identification Fishing System Based on Hog Super Pixel Feature Extraction

DCENet-based low-light image enhancement improved by spiking encoding and convLSTM

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