Underwater image processing and analysis: A review

Jian, Muwei; Liu, Xiangyu; Luo, Hanjiang; Lu, Xiangwei; Yu, Hui; Dong, Junyu

doi:10.1016/j.image.2020.116088

Cited by 140 publications

(54 citation statements)

References 73 publications

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“…For mAP, we also used the five metrics including mAP 50 , mAP 75 , mAP s , mAP m , and mAP l . Among them, mAP 50 and mAP 75 represent mAP with confidence levels of 0.5 and 0.75, respectively, while mAP s , mAP m , and mAP l represent mAP with object areas less than 32 2 , between 32 2 and 96 2 , and larger than 96 2 , respectively.…”

Section: Evaluation Metricsmentioning

confidence: 99%

“…Based on protecting the marine ecosystem, various methods of marine litter cleanup have been proposed and adopted by different environmental departments within government agencies, but they are not widely used. With the development of deep learning, computer vision offers a viable solution for this class of tasks [ 2 ], which are achieved by applying autonomous underwater detectors fitted with object detection algorithms. It can detect and locate the garbage in the seabed, thus making automatic or manned garbage cleanup possible.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

An Embeddable Algorithm for Automatic Garbage Detection Based on Complex Marine Environment

Deng

Ergu

Liu

et al. 2021

Sensors

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With the continuous development of artificial intelligence, embedding object detection algorithms into autonomous underwater detectors for marine garbage cleanup has become an emerging application area. Considering the complexity of the marine environment and the low resolution of the images taken by underwater detectors, this paper proposes an improved algorithm based on Mask R-CNN, with the aim of achieving high accuracy marine garbage detection and instance segmentation. First, the idea of dilated convolution is introduced in the Feature Pyramid Network to enhance feature extraction ability for small objects. Secondly, the spatial-channel attention mechanism is used to make features learn adaptively. It can effectively focus attention on detection objects. Third, the re-scoring branch is added to improve the accuracy of instance segmentation by scoring the predicted masks based on the method of Generalized Intersection over Union. Finally, we train the proposed algorithm in this paper on the Transcan dataset, evaluating its effectiveness by various metrics and comparing it with existing algorithms. The experimental results show that compared to the baseline provided by the Transcan dataset, the algorithm in this paper improves the mAP indexes on the two tasks of garbage detection and instance segmentation by 9.6 and 5.0, respectively, which significantly improves the algorithm performance. Thus, it can be better applied in the marine environment and achieve high precision object detection and instance segmentation.

show abstract

Section: Evaluation Metricsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An Embeddable Algorithm for Automatic Garbage Detection Based on Complex Marine Environment

Deng

Ergu

Liu

et al. 2021

Sensors

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show abstract

“…In fact, a very large number of common vision tasks, not limited to underwater scenes, require high image quality, such as image matching [1], visual detection [2,3], depth estimation [4], etc. Therefore, underwater image enhancement (UIE) is an essential technique in the underwater vision community [6].…”

Section: Introductionmentioning

confidence: 99%

Underwater image enhancement with latent consistency learning‐based color transfer

Yang

Tian

et al. 2022

IET Image Processing

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Due to the inevitable wavelength-dependent light absorption and forward/backward scattering, underwater images usually suffer severe color distortion and are hazy. It has become quite necessary to improve the visual quality of underwater images for both underwater observation and operation. Traditional enhancement methods and existing deep learningbased approaches to underwater image enhancement usually produce unsatisfactory results for photographs taken in complicated, wild underwater scenes. In such scenes, complex and diverse degradation-enhancement mappings are often difficult to model, especially since there are very limited samples available for learning. Inspired by the success of colortransfer techniques, it is found that clear template image-assisted color transfer is a promising strategy for underwater image enhancement, including not only color correction but also contrast and visibility improvement. Therefore, instead of directly learning the complex deep enhancement models, it is proposed to select proper color-transfer templates by learning the latent consistency between the templates and the raw underwater images. The proposed new enhancement strategy alleviates the problem caused by incomplete colorcorrection models and provides more stable enhancements by utilizing color transfer with consideration of global color distribution consistency and local visual contrast. Comprehensive experiments conducted on UIEB, RUIE, URPC and SQUID datasets demonstrate the good performance and great potential of the proposed new underwater image enhancement strategy.

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“…Various methods have been developed in past years to improve the visual quality of underwater images [1][2][3]. These methods can be roughly grouped as non-model-based, modelbased, and deep learning-based methods.…”

Section: Introductionmentioning

confidence: 99%

“…These methods can be roughly grouped as non‐model‐based, model‐based, and deep learning‐based methods. The non‐model‐based methods such as histogram equalization, homomorphic filtering, and retinal‐based enhancement algorithms, do not consider the image degradation mechanism, and directly process the pixels by highlighting the features of interest in the image and suppressing some unimportant features [2]. However, without considering the image degradation mechanism, colour distortion and supersaturation may remain occur, resulting in the loss of some image information.…”

Section: Introductionmentioning

confidence: 99%

Integrating deep learning and traditional image enhancement techniques for underwater image enhancement

Shi

Wang

Zhou

et al. 2022

IET Image Processing

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Underwater images usually suffer from colour distortion, blur, and low contrast, which hinder the subsequent processing of underwater information. To address these problems, this paper proposes a novel approach for single underwater images enhancement by integrating data‐driven deep learning and hand‐crafted image enhancement techniques. First, a statistical analysis is made on the average deviation of each channel of input underwater images to that of its corresponding ground truths, and it is found that both the red channel and the green channel of an underwater image contribute to its colour distortion. Concretely, the red channel of an underwater image is usually seriously attenuated, and the green channel is usually over strengthened. Motivated by such an observation, an attention mechanism guided residual module for underwater image colour correction is proposed, where the colour of the red channel of the underwater image and that of the green channel is compensated in a different way, respectively. Coupled with an attention mechanism, the residual module can adaptively extract and integrate the most discriminative features for colour correction. For scene contrast enhancement and scene deblurring, the traditional image enhancement techniques such as CLAHE (contrast limited adaptive histogram equalization) and Gamma correction are coupled with a multi‐scale convolutional neural network (MSCNN), where CLAHE and Gamma correction are used as complement to deal with the complex and changeable underwater imaging environment. Experiments on synthetic and real underwater images demonstrate that the proposed method performs favourably against the state‐of‐the‐art underwater image enhancement methods.

show abstract

Underwater image processing and analysis: A review

Cited by 140 publications

References 73 publications

An Embeddable Algorithm for Automatic Garbage Detection Based on Complex Marine Environment

An Embeddable Algorithm for Automatic Garbage Detection Based on Complex Marine Environment

Underwater image enhancement with latent consistency learning‐based color transfer

Integrating deep learning and traditional image enhancement techniques for underwater image enhancement

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