Single underwater image enhancement based on differential attenuation compensation

Lai, Yunting; Zhou, Zhuang; Su, Binghua; Xuanyuan, Zhe; Tang, Jialin; Yan, Jialin; Liang, Wanxin; Chen, Jiongjiang

doi:10.3389/fmars.2022.1047053

Cited by 17 publications

(9 citation statements)

References 38 publications

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“…Due to this phenomenon, it is reasonable to assume that, in most cases, underwater images have the most information on the blue and green channels. Based on a previous work, we have concluded that the main hue of the image correlates with the mean of each channel (Lai et al, 2022) and that, by determining the mean value of the channels, a more appropriate transmission map can be obtained. On the other hand, the channel with the highest mean value also indicates the least degree of absorption; therefore, its maximum should be limited in color correction and the minimum of the other channels should be increased to better correct the color deviations of the image.…”

Section: Adaptive Correction Of Channel Differential and Fusionmentioning

confidence: 94%

Single underwater image enhancement based on adaptive correction of channel differential and fusion

Zhao

Zhou²,

Lai³

et al. 2023

Front. Mar. Sci.

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Clear underwater images are necessary in many underwater applications, while absorption, scattering, and different water conditions will lead to blurring and different color deviations. In order to overcome the limitations of the available color correction and deblurring algorithms, this paper proposed a fusion-based image enhancement method for various water areas. We proposed two novel image processing methods, namely, an adaptive channel deblurring method and a color correction method, by limiting the histogram mapping interval. Subsequently, using these two methods, we took two images from a single underwater image as inputs of the fusion framework. Finally, we obtained a satisfactory underwater image. To validate the effectiveness of the experiment, we tested our method using public datasets. The results showed that the proposed method can adaptively correct color casts and significantly enhance the details and quality of attenuated underwater images.

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Section: Adaptive Correction Of Channel Differential and Fusionmentioning

confidence: 94%

Single underwater image enhancement based on adaptive correction of channel differential and fusion

Zhao

Zhou²,

Lai³

et al. 2023

Front. Mar. Sci.

Self Cite

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“…Contrast Stretching, on the other hand, expands the contrast between light and dark areas within the image. This can be particularly beneficial for highlighting coral structures that might otherwise blend into the background under certain lighting conditions (Lai et al, 2022).…”

Section: Datasetmentioning

confidence: 99%

Fine-Grained Analysis of Coral Instance Segmentation using YOLOv8 Models

Hassanudin,

Utomo,

Apriyanto

2024

SinkrOn

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Within the geographical boundaries of Indonesia, coral reefs flourish as intricate ecosystems bustling with a variety of marine creatures that play a crucial role, in preserving biodiversity. However this delicate harmony faces threats from climate change and human activities, leading to the risk of species loss. Despite growing awareness surrounding these challenges effectively and swiftly monitoring conditions remains a task. Existing methods for assessing corals often fall short due to requiring extensive specialist knowledge, lacking large-scale coverage, and being costly to implement. To tackle these obstacles this research suggests an approach for automated reef monitoring using instance segmentation with a YOLOv8 model. Leveraging YOLOv8 segmentation capabilities enables efficient analysis of corals. A systematic process is employed involving data collection, preparation (including techniques like Histogram Equalization), training the model on a reef dataset, model evaluation and enhancing the segmentation mask. The outcomes reveal the YOLOv8m Pp model with 96.7% precision 95.9% recall rate and a mean Average Precision (mAP50) score of 98.2%. This study demonstrates the potential of YOLOv8 to accurately segment instances for monitoring reefs in Indonesia, hence facilitating improved conservation strategies.

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“…To demonstrate that AUIE-GAN promotes the development of the UIE task, we compared it with the mainstream UIE methods in recent years, which include FE (2012) [15], RCP (2015) [16], DAC (2022) [47] among traditional methods, FUnIE-GAN (2020) [35], MLFcGAN (2020) [36], UWGAN (2020) [2], Shallow-UWNet (2021) [29], NUˆ2NET (2022/SOTA) [30], PUIE-NET(2022/SOTA) [31] and Semi-UIR (2023/SOTA) [48] among deep learning-based methods.…”

Section: Implementations and Baselinesmentioning

confidence: 99%

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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Single underwater image enhancement based on differential attenuation compensation

Cited by 17 publications

References 38 publications

Single underwater image enhancement based on adaptive correction of channel differential and fusion

Single underwater image enhancement based on adaptive correction of channel differential and fusion

Fine-Grained Analysis of Coral Instance Segmentation using YOLOv8 Models

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

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