An Overview of Underwater Vision Enhancement: From Traditional Methods to Recent Deep Learning

Hu, Kai; Weng, Chenghang; Zhang, Yanwen; Jin, Junlan; Xia, Qingfeng

doi:10.3390/jmse10020241

Cited by 84 publications

(38 citation statements)

References 115 publications

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“…Modern semantic VSLAM systems cannot do without the help of deep learning, and feature attributes and association relations obtained through learning can be used in different tasks [138]. As an important branch of machine learning, deep learning has achieved remarkable results in image recognition [139], semantic understanding [140], image matching [141], 3D reconstruction [142], and other tasks. The application of deep learning in computer vision can greatly ease the problems encountered by traditional methods [143].…”

Section: Semantic Vslammentioning

confidence: 99%

An Overview on Visual SLAM: From Tradition to Semantic

et al. 2022

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Visual SLAM (VSLAM) has been developing rapidly due to its advantages of low-cost sensors, the easy fusion of other sensors, and richer environmental information. Traditional visionbased SLAM research has made many achievements, but it may fail to achieve wished results in challenging environments. Deep learning has promoted the development of computer vision, and the combination of deep learning and SLAM has attracted more and more attention. Semantic information, as high-level environmental information, can enable robots to better understand the surrounding environment. This paper introduces the development of VSLAM technology from two aspects: traditional VSLAM and semantic VSLAM combined with deep learning. For traditional VSLAM, we summarize the advantages and disadvantages of indirect and direct methods in detail and give some classical VSLAM open-source algorithms. In addition, we focus on the development of semantic VSLAM based on deep learning. Starting with typical neural networks CNN and RNN, we summarize the improvement of neural networks for the VSLAM system in detail. Later, we focus on the help of target detection and semantic segmentation for VSLAM semantic information introduction. We believe that the development of the future intelligent era cannot be without the help of semantic technology. Introducing deep learning into the VSLAM system to provide semantic information can help robots better perceive the surrounding environment and provide people with higher-level help.

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Section: Semantic Vslammentioning

confidence: 99%

An Overview on Visual SLAM: From Tradition to Semantic

et al. 2022

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“…Step 2. Calculate the dark channel prior weight coefficient DCP w of the fused image according to (8), and then use (9) to calculate the weight coefficient i W of the second fusion step.…”

Section: Dcpother W Andmentioning

confidence: 99%

“…Different from the deep sea, restoration and enhancement underwater images is a challenging problem owing to the ocean current disturbance in the epicontinental sea and the light propagation, absorption and scattering by micro suspended particles in epicontinental sea [5]. Special environment under epicontinental sea water provokes several combined degradation in images including color attenuation, blurring, low contrast, and their interaction (e.g., color distortion and haze effects) , these are important problems for underwater image enhancement [6][7][8][9][10]. In order to conquer the color imbalance, blurring, low contrast, etc., a deep retinal decomposion network for underwater image enhancement was proposed and convolutional neural network was designed to estimate the illumination and get reflectance, and color balance and illumination correction was performed on the decomposed reflectance and illumination in [11].…”

Section: Introductionmentioning

confidence: 99%

Underwater Image Enhancement in Epicontinental Sea Based on the Improved Algorithm of Dark Channel

Zhu¹

2022

Preprint

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Enhancing underwater images in epicontinental sea is a challenging problem owing to the influence of ocean currents, the refraction, absorption and scattering of light by suspended particles, and the weak illumination intensity. Recently, different methods have relied on the underwater image formation model and deep learning techniques to restore the underwater image, but they tend to degrade underwater image, interference of background clutter and miss boundary details of blue regions. Improved image fusion and enhancement algorithm based on a priori dark channel is proposed in this paper. Image edge features sharpening and dark detail enhancement by homomorphism filtering in CIELab color space is realized. In RGB color space, the multi-scale retinal with color restoration (MSRCR) algorithm is used to improve color deviation and enhance color saturation, and the contrast-limited adaptive histogram equalization (CLAHE) algorithm is used to de fog and enhance image contrast. Finally, according to the dark channel images of the three processing results, the final enhanced image is obtained by linear fusion of multiple images and multiple channels. Experimental results demonstrate the effectiveness and practicality of the proposed method on various datasets.

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“…However, these methods are effective only in specific scenarios and have limited robustness in complex scenarios. With the rapid development of deep learning, convolutional neural network (CNN)-and Transformer-based methods have been widely used in the field of image processing [8][9][10][11][12][13][14].…”

Section: Introductionmentioning

confidence: 99%

Multi-Level Wavelet-Based Network Embedded with Edge Enhancement Information for Underwater Image Enhancement

Sun

Meng

Tian

2022

JMSE

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As an image processing method, underwater image enhancement (UIE) plays an important role in the field of underwater resource detection and engineering research. Currently, the convolutional neural network (CNN)- and Transformer-based methods are the mainstream methods for UIE. However, CNNs usually use pooling to expand the receptive field, which may lead to information loss that is not conducive to feature extraction and analysis. At the same time, edge blurring can easily occur in enhanced images obtained by the existing methods. To address this issue, this paper proposes a framework that combines CNN and Transformer, employs the wavelet transform and inverse wavelet transform for encoding and decoding, and progressively embeds the edge information on the raw image in the encoding process. Specifically, first, features of the raw image and its edge detection image are extracted step by step using the convolution module and the residual dense attention module, respectively, to obtain mixed feature maps of different resolutions. Next, the residual structure Swin Transformer group is used to extract global features. Then, the resulting feature map and the encoder’s hybrid feature map are used for high-resolution feature map reconstruction by the decoder. The experimental results show that the proposed method can achieve an excellent effect in edge information protection and visual reconstruction of images. In addition, the effectiveness of each component of the proposed model is verified by ablation experiments.

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An Overview of Underwater Vision Enhancement: From Traditional Methods to Recent Deep Learning

Cited by 84 publications

References 115 publications

An Overview on Visual SLAM: From Tradition to Semantic

An Overview on Visual SLAM: From Tradition to Semantic

Underwater Image Enhancement in Epicontinental Sea Based on the Improved Algorithm of Dark Channel

Multi-Level Wavelet-Based Network Embedded with Edge Enhancement Information for Underwater Image Enhancement

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