A Survey of Restoration and Enhancement for Underwater Images

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Images captured from underwater environment always suffer from color distortion, detail loss, and contrast reduction due to the medium scattering and absorption. This paper introduces an enhancement approach to improve the visual quality of underwater images, which does not require any dedicated devices or additional information more than the native single image. The proposed strategy consists of two steps: an improved white-balancing approach and an artificial multiple underexposure image fusion strategy for underwater imaging. In our white-balancing approach, the optimal color-compensated approach is determined by the sum of the Underwater Color Image Quality Evaluation (UCIQE) and the Underwater Image Quality Measure (UIQM). We get an optimal white-balanced version of the input by combining the well-known Gray World assumption and the optimal channel-compensated approach. In our artificial multiple underexposure image fusion strategy, first the gamma-correction operation is adopted to generate multiple underexposure versions. Then we propose to use 'contrast', 'saturation', and 'well-exposedness' as three weights, to be blended into the well-known multi-scale fusing scheme. Images enhanced by our strategy have a better visual quality than some state-of-the-art underwater dehazing techniques, through our validation with a wide range of qualitative and quantitative evaluations. INDEX TERMS Underwater image enhancement, artificial multiple underexposure image fusion, color correction, contrast enhancement, detail recovery.

Section: B Mainstream Of Single Underweater Image Methodsmentioning

confidence: 99%

Section: A Underwater Imaging Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Novel Two-Step Strategy Based on White-Balancing and Fusion for Underwater Image Enhancement

Tao

Dong

2020

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“…It is based on the study of a large number of samples, that is, the deep and specific feature representation of the dataset can be obtained. Convolution neural network (CNN) [9] has been broadly applied in the field of computer vision, such as classification [10], target detection [11], [39], segmentation [12] [40], image quality enhancement [36][37][38], etc. .…”

Section: Introductionmentioning

confidence: 99%

Contextual Feature Constrained Semantic Face Completion With Paired Discriminator

Yang

Xue

et al. 2021

Image semantic completion is to employ remaining image information to restore the damaged or missing areas. Face completion task is usually more challenging than other image inpainting problems as it requires stronger semantic consistency. We proposed a contextual feature constrained DCGAN with paired discriminator to inpaint damaged face images, which is capable of overcoming the DCGAN's shortages of insufficient feature learning capability and unstable training process. Our network is composed of an encoderdecoder generator and a local and global (paired) adversarial discriminator. Generator is used to produce missing parts, and the paired discriminators evaluate the authenticity of the local generated parts and the consistency of the global completed image respectively. In addition, the proposed network generates the image by optimizing the generator with three types of loss functions, i.e., image reconstruction loss, paired feature matching losses and paired feature reconstruction losses. The experiments on celebA and Stanford Cars dataset show that our model could generate reasonable repair results and that on some of the evaluation indicators were higher than baseline on most test datasets.

“…In order to mitigate the appearance change caused by the change of perspective, a multiview generation model based on GAN is introduced into the existing PRI model. Recently, generative adversarial networks (GANs) [18] have achieved excellent results in image generation [19], [20], [21], image recovery [22], image dehazing [23], [24] and style transfer [25], [26] thanks to their powerful image processing capabilities. In addition, GAN is widely used in multiview generation, and it has been able to generate higher-quality images.…”

Section: Introductionmentioning

confidence: 99%

A Novel Pedestrian Reidentification Method Based on a Multiview Generative Adversarial Network

Guo

Pang

et al. 2020

Emerging deep learning (DL) techniques have greatly improved pedestrian reidentification (PRI) performance. However, the existing DL-based PRI methods cannot learn robust feature representations owing to the single view of query images and the limited number of extractable features. Inspired by generative adversarial networks (GANs), this paper proposes a novel PRI method based on a pedestrian multiview GAN (PmGAN) and a classification recognition network (CRN). The PmGAN consists of three generators and one multiclass discriminator. The three generators produce pedestrian images from the front, side and back, while the multiclass discriminator determines whether the input image is a real image or a generated image. In addition to expanding the existing pedestrian datasets, the PmGAN can generate pedestrian images from front, side and back views based on a given query image and thereby increase the feature semantic space of the query image. To verify the performance of our method, the PmGAN was compared with mainstream pedestrian image generation models, and then the proposed method was contrasted with mainstream PRI methods. The results show that the proposed PmGAN greatly improved the performance of mainstream PRI methods. For example, the combination of the PmGAN and Pyramidal increased the mean average precision (mAP) on three common datasets by 1.2% on average. The research findings provide new insights into the application of multiview generation in PRI tasks. INDEX TERMS deep learning (DL), generative adversarial networks (GANs), image generation, pedestrian reidentification (PRI), classification recognition network (CRN)