FW-GAN: Underwater image enhancement using generative adversarial network with multi-scale fusion

Wu, Junjun; Li, Xilin; Lu, Qinghua; Lin, Zhongqin; Qin, Ningwei; Shi, Qingwu

doi:10.1016/j.image.2022.116855

Cited by 25 publications

(10 citation statements)

References 33 publications

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“…These methods can obtain high-quality enhanced images by changing the pixel values in a single degraded underwater image [6]. After changing, the image has a satisfactory distribution.…”

Section: Enhancement-based Methodsmentioning

confidence: 99%

“…The MLFcGAN [36] used global features to enhance local features at each scale to perform color correction and image detail preservation. Wu et al [6] proposed a multi-scale fusion enhancement method based on GANs to improve the enhancement and generalization ability of the model by encoding and combining three different priors.…”

Section: Deep Learning-based Methodsmentioning

confidence: 99%

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AUIE–GAN: Adaptive Underwater Image Enhancement Based on Generative Adversarial Networks

Guan

Lai

et al. 2023

JMSE

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Underwater optical imaging devices are often affected by the complex underwater environment and the characteristics of the water column, which leads to serious degradation and distortion of the images they capture. Deep learning-based underwater image enhancement (UIE) methods reduce the reliance on physical parameters in traditional methods and have powerful fitting capabilities, becoming a new baseline method for UIE tasks. However, the results of these methods often suffer from color distortion and lack of realism because they tend to have poor generalization and self-adaptation capabilities. Generating adversarial networks (GANs) provides a better fit and shows powerful capabilities on UIE tasks. Therefore, we designed a new network structure for the UIE task based on GANs. In this work, we changed the learning of the self-attention mechanism by introducing a trainable weight to balance the effect of the mechanism, improving the self-adaptive capability of the model. In addition, we designed a feature extractor based on residuals with multi-level residuals for better feature recovery. To further improve the performance of the generator, we proposed a dual path discriminator and a loss function with multiple weighted fusions to help model fitting in the frequency domain, improving image quality. We evaluated our method on the UIE task using challenging real underwater image datasets and a synthetic image dataset and compared it to state-of-the-art models. The method ensures increased enhancement quality, and the enhancement effect of the model for different styles of images is also relatively stable.

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“…These methods can obtain high-quality enhanced images by changing the pixel values in a single degraded underwater image [6]. After changing, the image has a satisfactory distribution.…”

Section: Enhancement-based Methodsmentioning

confidence: 99%

Section: Deep Learning-based Methodsmentioning

confidence: 99%

AUIE–GAN: Adaptive Underwater Image Enhancement Based on Generative Adversarial Networks

Guan

Lai

et al. 2023

JMSE

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“…In recent years, with the development of marine research and the exploration of marine resources, underwater robots have been gradually applied in the fields of 3D reconstruction of submarine scenes [1] , marine life classification [2] , and target identification [3] . In the complex underwater environment, there are a large number of suspended particles such as sediment, microorganisms, and plankton.…”

Section: Introductionmentioning

confidence: 99%

“…Firstly, in order to obtain the turbidity information and color deviation degree of the water body in the experiment, it is used to make the water body classification label. In the experiment, we used three modulated lasers of red, green, and blue (1) as light sources respectively, and designed a photoelectric detection circuit. By measuring the intensity of scattered light with different wavelengths and different scattering angles, the turbidity information of water and the color cast information of water bodies can be accurately evaluated at the same time.…”

Section: Introductionmentioning

confidence: 99%

Underwater image enhancement based on deep learning water body pre-classification

Fu,

Yu,

Liu

et al. 2024

Sixth Conference on Frontiers in Optical Imaging and Technology: Applications of Imaging Technologies

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Image enhancement plays an important role in the field of underwater vision. Numerous underwater image enhancement algorithms have been proposed in the last few years, which have achieved some good results in processing specific underwater images. However, the effectiveness of these algorithms to cope with different underwater environments remains uncertain. To address this issue, we propose a water body classification label based on scattering characteristics and construct a dataset with a large number of photos of experiments in different water conditions. Meanwhile, based on different types of water bodies we also trained a network model which has thirteen classifications. Using this dataset, we study comprehensively these underwater image enhancement algorithms qualitatively and quantitatively and match each type of underwater image with an optimal underwater image enhancement algorithm. An underwater image enhancement algorithm based on deep-learning water pre-classification is then proposed. This adapted algorithm is applied to process real underwater images captured by the underwater robot and obtains good processing results. It also contributes to further research on underwater image enhancement.

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“…Gao et al [13] proposed a new image enhancement method to restore image color by fusing the contrast corrected image and sharpened image. Wu et al [14] proposed an underwater image enhancement method based on generative adversarial network with multi-scale fusion. However, due to the dark environment in deep water, underwater images commonly suffer from low brightness.…”

Section: Introductionmentioning

confidence: 99%

Underwater Image Enhancement Based on Color Balance and Multi-Scale Fusion

Chen

Zhang

2022

IEEE Photonics J.

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Underwater light absorption and scattering lead to color deviation, low brightness, fuzzy details and low contrast of underwater images. In this contribution, an underwater image enhancement algorithm based on color balance and multi-scale fusion is proposed. Firstly, a color balance method is used to correct the image color. Then, based on an improved dark channel prior algorithm, the local contrast information of the color balanced image is used to derive two atmospheric lights and transmission maps, which are transformed into the images with enhanced contrast and brightness, respectively. Finally, the multi-scale fusion method is adopted to fuse the contrast enhanced image and the brightness improved image according to the weights. The proposed algorithm is compared with other underwater image enhancement algorithms in qualitative and quantitative evaluation. Experimental results show that the proposed algorithm can effectively eliminate color deviation, remove dark areas, and improve brightness and contrast of underwater images. The enhanced images generated by the proposed algorithm are superior to those generated by other algorithms in indicators of PSNR, SSIM, UIQM and UCIQE. Thus, the proposed algorithm is suitable for underwater image enhancement.

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FW-GAN: Underwater image enhancement using generative adversarial network with multi-scale fusion

Cited by 25 publications

References 33 publications

AUIE–GAN: Adaptive Underwater Image Enhancement Based on Generative Adversarial Networks

AUIE–GAN: Adaptive Underwater Image Enhancement Based on Generative Adversarial Networks

Underwater image enhancement based on deep learning water body pre-classification

Underwater Image Enhancement Based on Color Balance and Multi-Scale Fusion

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