Human-Visual-System-Inspired Underwater Image Quality Measures

Panetta, Karen; Gao, Chen; Agaian, Sos S.

doi:10.1109/joe.2015.2469915

Cited by 1,028 publications

(401 citation statements)

References 36 publications

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“…2) Non-reference Evaluation: We employ two nonreference metrics (i.e., UCIQE [59] and UIQM [60]) which are usually used for underwater image quality evaluation [39], [40], [45], [46]. A higher UCIQE score indicates the result has better balance among the chroma, saturation, and raws fusion-based retinex-based UDCP Red Channel histogram prior blurriness-based GDCP reference images two-step-based regression-based Fig.…”

Section: B Quantitative Evaluationmentioning

confidence: 99%

An Underwater Image Enhancement Benchmark Dataset and Beyond

Guo

Ren

et al. 2020

IEEE Trans. on Image Process.

1,242

672

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Underwater image enhancement has been attracting much attention due to its significance in marine engineering and aquatic robotics. Numerous underwater image enhancement algorithms have been proposed in the last few years. However, these algorithms are mainly evaluated using either synthetic datasets or few selected real-world images. It is thus unclear how these algorithms would perform on images acquired in the wild and how we could gauge the progress in the field. To bridge this gap, we present the first comprehensive perceptual study and analysis of underwater image enhancement using large-scale real-world images. In this paper, we construct an Underwater Image Enhancement Benchmark (UIEB) including 950 realworld underwater images, 890 of which have the corresponding reference images. We treat the rest 60 underwater images which cannot obtain satisfactory reference images as challenging data. Using this dataset, we conduct a comprehensive study of the stateof-the-art underwater image enhancement algorithms qualitatively and quantitatively. In addition, we propose an underwater image enhancement network (called Water-Net) trained on this benchmark as a baseline, which indicates the generalization of the proposed UIEB for training Convolutional Neural Networks (CNNs). The benchmark evaluations and the proposed Water-Net demonstrate the performance and limitations of state-of-the-art algorithms, which shed light on future research in underwater image enhancement. The dataset and code are available at https://li-chongyi.github.io/proj benchmark.html.

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Section: B Quantitative Evaluationmentioning

confidence: 99%

An Underwater Image Enhancement Benchmark Dataset and Beyond

Guo

Ren

et al. 2020

IEEE Trans. on Image Process.

1,242

672

View full text Add to dashboard Cite

show abstract

“…The results indicate that FUnIE-GAN performs best on both PSNR and SSIM metrics. We conduct a similar analysis for Underwater Image Quality Measure (UIQM) [36,30], which quantifies underwater image colorfulness, sharpness, and contrast. We present the results in Table 2, which indicates that although FUnIE-GAN-UP performs better than CycleGAN, its UIQM values on the the paired dataset are relatively poor.…”

Section: Quantitative Evaluationmentioning

confidence: 99%

Fast Underwater Image Enhancement for Improved Visual Perception

Islam

Xia

Sattar

2020

IEEE Robot. Autom. Lett.

826

414

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In this paper, we present a conditional generative adversarial network-based model for real-time underwater image enhancement. To supervise the adversarial training, we formulate an objective function that evaluates the perceptual image quality based on its global content, color, local texture, and style information. We also present EUVP, a largescale dataset of a paired and an unpaired collection of underwater images (of 'poor' and 'good' quality) that are captured using seven different cameras over various visibility conditions during oceanic explorations and human-robot collaborative experiments. In addition, we perform several qualitative and quantitative evaluations which suggest that the proposed model can learn to enhance underwater image quality from both paired and unpaired training. More importantly, the enhanced images provide improved performances of standard models for underwater object detection, human pose estimation, and saliency prediction. These results validate that it is suitable for real-time preprocessing in the autonomy pipeline by visually-guided underwater robots. The model and associated training pipelines are available at https://github.com/xahidbuffon/ funie-gan.

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“…A variety of algorithms have been developed to enhance underwater images by (a) dehazing the image (Trucco and Olmos-Antillon, 2006;Ancuti et al, 2012;Chiang and Chen, 2012), (b) compensating non-uniform illumination (Eustice et al, 2002;Singh et al, 2007;Schoening et al, 2012b;Bryson et al, 2015) and (c) increasing the image contrast and correcting the color shift (Zuiderveld, 1994;Eustice et al, 2002;Chambah et al, 2003;Trucco and Olmos-Antillon, 2006;Singh et al, 2007;Petit et al, 2009;Iqbal et al, 2010;Ancuti et al, 2012;Chiang and Chen, 2012;Schoening et al, 2012b;Abdul Ghani and Mat Isa, 2014;Bryson et al, 2015). Although in the last years new methods have been proposed to compare and rank the quality of different algorithms (Osterloff et al, 2014;Panetta et al, 2015;Yang and Sowmya, 2015), the selection of the best algorithm can be challenging. Reviews of underwater image enhancement algorithms can be found in (Schettini and Corchs, 2010;Wang et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

RecoMIA—Recommendations for Marine Image Annotation: Lessons Learned and Future Directions

2016

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Marine imaging is transforming into a sensor technology applied for high throughput sampling. In the context of habitat mapping, imaging establishes thereby an important bridge technology regarding the spatial resolution and information content between physical sampling gear (e.g., box corer, multi corer) on the one end and hydro-acoustic sensors on the other end of the spectrum of sampling methods. In contrast to other scientific imaging domains, such as digital pathology, there are no protocols and reports available that guide users (often referred to as observers) in the non-trivial process of assigning semantic categories to whole images, regions, or objects of interest (OOI), which is referred to as annotation. These protocols are crucial to facilitate image analysis as a robust scientific method. In this article we will review the past observations in manual Marine Image Annotations (MIA) and provide (a) a guideline for collecting manual annotations, (b) definitions for annotation quality, and (c) a statistical framework to analyze the performance of human expert annotations and to compare those to computational approaches.

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Human-Visual-System-Inspired Underwater Image Quality Measures

Cited by 1,028 publications

References 36 publications

An Underwater Image Enhancement Benchmark Dataset and Beyond

An Underwater Image Enhancement Benchmark Dataset and Beyond

Fast Underwater Image Enhancement for Improved Visual Perception

RecoMIA—Recommendations for Marine Image Annotation: Lessons Learned and Future Directions

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