A Two-Stage Underwater Enhancement Network Based on Structure Decomposition and Characteristics of Underwater Imaging

Wu, Shengcong; Luo, Ting; Jiang, Gangyi; Yu, Mei; Xu, Haiyong; Zhu, Zhongkui; Song, Yang

doi:10.1109/joe.2021.3064093

Cited by 64 publications

(23 citation statements)

References 51 publications

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“…With the rise of deep learning in computer vision and image processing, there are some deep learning methods based on a large number of training datasets to enhance underwater image quality [13][14][15][16][17][18]. Zhu et al [13] proposed a generative adversarial network, called CycleGAN, which uses a set of aligned image pair training sets to learn the mapping between an input image and output image, to realize the transformation of image style.…”

Section: Underwater Image Enhancement and Restorationmentioning

confidence: 99%

“…Li et al [16] proposed a fusion generated adversarial network (FGAN) for enhancing underwater images. Wu et al [17] decomposed the original underwater image into high frequency and low frequency, based on the underwater imaging model. Then, a two-stage underwater enhancement network (UWCNN-SD) of preliminary enhancement network and refinement network is proposed.…”

Section: Underwater Image Enhancement and Restorationmentioning

confidence: 99%

“…It can be found that different methods show different appearances. In the following subsection, subjective experiments are set and implemented to evaluate the images generated by these enhancement methods, quantita- To improve the quality of underwater images, different image enhancement methods, restoration methods, and deep learning methods were used to enhance the original underwater images, including RB [4], UDCP [9], UIBLA [11], RED [10], CycleGAN [13], WSCT [15], UGAN [14], FGAN [16], UWCNN-SD [17]. Here, the author published all codes of underwater image enhancement and restoration, and there is no additional debugging for the code and default parameters.…”

Section: Underwater Image Enhancement Database (Uiqe)mentioning

confidence: 99%

“…Here, we describe the statistical interaction between normalized GM and LOG features, as described in Equations ( 16) and (17). Figure 8 Considering the characteristics of the underwater images, the dark and bright regions are compared in images.…”

Section: Gradient Amplitude and Laplacian Of Gaussianmentioning

confidence: 99%

“…To address this problem, some underwater image enhancement and restoration methods are proposed [4][5][6][7][8][9][10][11][12][13][14][15][16][17][18]. However, due to the complexity of the underwater environment, 1.…”

Section: Introductionmentioning

confidence: 99%

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Subjective and Objective Quality Evaluation for Underwater Image Enhancement and Restoration

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Since underwater imaging is affected by the complex water environment, it often leads to severe distortion of the underwater image. To improve the quality of underwater images, underwater image enhancement and restoration methods have been proposed. However, many underwater image enhancement and restoration methods produce over-enhancement or under-enhancement, which affects their application. To better design underwater image enhancement and restoration methods, it is necessary to research the underwater image quality evaluation (UIQE) for underwater image enhancement and restoration methods. Therefore, a subjective evaluation dataset for an underwater image enhancement and restoration method is constructed, and on this basis, an objective quality evaluation method of underwater images, based on the relative symmetry of underwater dark channel prior (UDCP) and the underwater bright channel prior (UBCP) is proposed. Specifically, considering underwater image enhancement in different scenarios, a UIQE dataset is constructed, which contains 405 underwater images, generated from 45 different underwater real images, using 9 representative underwater image enhancement methods. Then, a subjective quality evaluation of the UIQE database is studied. To quantitatively measure the quality of the enhanced and restored underwater images with different characteristics, an objective UIQE index (UIQEI) is used, by extracting and fusing four groups of features, including: (1) the joint statistics of normalized gradient magnitude (GM) and Laplacian of Gaussian (LOG) features, based on the underwater dark channel map; (2) the joint statistics of normalized gradient magnitude (GM) and Laplacian of Gaussian (LOG) features, based on the underwater bright channel map; (3) the saturation and colorfulness features; (4) the fog density feature; (5) the global contrast feature; these features capture key aspects of underwater images. Finally, the experimental results are analyzed, qualitatively and quantitatively, to illustrate the effectiveness of the proposed UIQEI method.

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