Joint Iterative Color Correction and Dehazing for Underwater Image Enhancement

Wang, Kun; Shen, Liquan; Lin, Yufei; Li, Mengyao; Zhao, Qijie

doi:10.1109/lra.2021.3070253

Cited by 31 publications

(6 citation statements)

References 36 publications

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“…By gaining change coefficients from dehazing highlights, shading elements and essential elements of crude pictures are continuously refined, which keeps up with shading adjusted during the dehazing system and further develops the clearness of pictures. Test results show that our organization is better than the current cutting-edge approaches for UIE and gives further developed execution to submerged article identification [8].…”

Section: ░ 2 Imaging Model In the Underwater Scenariomentioning

confidence: 97%

An Adaptive Technique for Underwater Image Enhancement with CNN and Ensemble Classifier

Gupta

Saxena

2022

IJEER

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Image Restoration is a significant phase to process images for their enhancement. Underwater photographs are subject to quality issues such as blurry photos, poor contrast, uneven lighting, etc. Image processing is crucial in the processing of these degraded images. This research introduced an ensemble-based classifier based on the bagging approach to enhance UW images. The support vector machine and random forest classifiers serve as the ensemble classifier's main classifiers. Additionally, to complement the feature optimization technique, the proposed ensemble classifier leverages particle swarm optimization. The feature selection method for the classifier is improved by the feature optimization process. To validate results, underwater images are collected by the Kaggle repository. In this process, Extreme Learning (EL) and Convolution Neural Network (CNN) are compared with suggested algorithms. The simulation results indicate that the proposed algorithm outperformed the existing work.

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Section: ░ 2 Imaging Model In the Underwater Scenariomentioning

confidence: 97%

An Adaptive Technique for Underwater Image Enhancement with CNN and Ensemble Classifier

Gupta

Saxena

2022

IJEER

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“…The future work is to synthesize the dataset and work on feasibility in other applications. [17] To solve degradation issues, an underwater image enhancement network via medium transmission-guided multi-color space embedding, called ucolor is proposed. A visual quality enhancement method for underwater images based on multi-feature prior fusion (MFPF), achieved by extracting and fusing multiple feature priors of underwater images.…”

Section: Literature Surveymentioning

confidence: 99%

Image Enhancement of Underwater Images using Deep Learning Techniques

Sravani¹,

Sai²,

Noomika³

et al. 2023

Int. J. Res. Publ. Rev.

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The quality of underwater images is very important for research purpose and ocean exploration. The market for high-quality underwater images is expanding exponentially. Despite this, there are a number of issues including blurriness, dim lighting, poor contrast and color fidelity. Light attenuation, absorption and scattering lower the quality. As a result, it is essential to enhance underwater images, and different image processing techniques are used for enhancement .The deep learning methodology used in this study improves image quality and produce the best outcomes when compared to conventional image enhancing techniques. The architecture used is GAN with the combination of Image processing algorithms as a preprocessing step for implementing deep learning models .The algorithms include Histogram Equalization and Dark Channel Prior. These methods normalizes the contrast of images and removes the hazing or dark parts of the images. The dataset used in this study is EUVP (Paired) which contains about 2186 real-world underwater images. Three parameters, UICM, MSE and PSNR are used to assess quality of enhanced images. The optimum technique for improving the images is determined by comparing the performance of the models after the dataset has been trained through them.

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“…Physical model-based methods focus on modeling the underwater image degradation process and estimating parameters based on the model. Such methods include the red channel prior [7] and the minimum information prior [8]. Physical model-based methods rely excessively on modeling of underwater imaging, which is usually based on certain a priori assumptions and has limitations in generalizing underwater images to different scenario applications.…”

Section: Introductionmentioning

confidence: 99%

An underwater image enhancement method based on Swin transformer

Yang,

Liu,

Zhang

2023

Third International Conference on Optics and Communication Technology (ICOCT 2023)

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An underwater image enhancement model based on Swin Transformer is designed to solve the problems of color shift, low contrast, detail loss and noise in underwater images caused by absorption and scattering effects during the propagation of light underwater. The model consists of two parts: feature extraction and image reconstruction. First, image features are extracted using a set of improved residual Swin Transformer modules, which allows the model to process images with fewer parameters and establish long-term dependencies between image features. Further, a parallel attention mechanism is introduced into the model to focus on both semantic and spatial information contained in underwater images to improve image enhancement. Experimental results show that compared with existing popular underwater image enhancement methods, the proposed Underwater Image Enhancement Method in this paper has advantages in both the number of network parameters and image enhancement accuracy and has made significant progress in correcting color bias, improving contrast, enhancing details and eliminating noise.

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Joint Iterative Color Correction and Dehazing for Underwater Image Enhancement

Cited by 31 publications

References 36 publications

An Adaptive Technique for Underwater Image Enhancement with CNN and Ensemble Classifier

An Adaptive Technique for Underwater Image Enhancement with CNN and Ensemble Classifier

Image Enhancement of Underwater Images using Deep Learning Techniques

An underwater image enhancement method based on Swin transformer

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