An Online Platform for Underwater Image Quality Evaluation

Li, Chau Yi; Mazzon, Riccardo; Cavallaro, Andrea

doi:10.1007/978-3-030-05792-3_4

Cited by 4 publications

(5 citation statements)

References 14 publications

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“…A thoroughly validated IQA measure, with a description of its applicable scenarios and limitations, would contribute to a standardised evaluation approach for underwater images and support progress in this field. To this end, to complement our survey, we provide a companion online platform, PUIQE [64], which supports underwater image filtering and evaluation.…”

Section: Discussionmentioning

confidence: 99%

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Underwater image filtering: methods, datasets and evaluation

Li,

Mazzon,

Cavallaro

2020

Preprint

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Underwater images are degraded by the selective attenuation of light that distorts colours and reduces contrast. The degradation extent depends on the water type, the distance between an object and the camera, and the depth under the water surface the object is at. Underwater image filtering aims to restore or to enhance the appearance of objects captured in an underwater image. Restoration methods compensate for the actual degradation, whereas enhancement methods improve either the perceived image quality or the performance of computer vision algorithms. The growing interest in underwater image filtering methods-including learning-based approaches used for both restoration and enhancement-and the associated challenges call for a comprehensive review of the state of the art. In this paper, we review the design principles of filtering methods and revisit the oceanology background that is fundamental to identify the degradation causes. We discuss image formation models and the results of restoration methods in various water types. Furthermore, we present taskdependent enhancement methods and categorise datasets for training neural networks and for method evaluation. Finally, we discuss evaluation strategies, including subjective tests and quality assessment measures. We complement this survey with a platform (https://puiqe.eecs.qmul.ac.uk/), which hosts state-of-the-art underwater filtering methods and facilitates comparisons.

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Section: Discussionmentioning

confidence: 99%

“…8). We complement our survey with an online Platform for Underwater Image Quality Evaluation (PUIQE) [64], which enables testing the filtering methods and evaluation measures covered in this survey. The novelty of this survey is highlighted in Table 1, which summarises and compares ours with previous surveys on this topic [65]- [72].…”

Section: Introductionmentioning

confidence: 99%

Underwater image filtering: methods, datasets and evaluation

Li,

Mazzon,

Cavallaro

2020

Preprint

Self Cite

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show abstract

“…These enhancements were subjective and tailored and, thus, might not adhere to aesthetic improvements in the conventional sense, such as those that score highly with objective reference metrics. Indeed, studies have shown that objective assessments of image quality do not always correspond well with subjective perception [ 34 , 35 ]. That being said, we found the enhancements in this study were also beneficial objectively.…”

Section: Methodsmentioning

confidence: 99%

Weibull Tone Mapping (WTM) for the Enhancement of Underwater Imagery

Game

Thompson

Finlayson

2023

Sensors

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Domain experts prefer interactive and targeted control-point tone mapping operations (TMOs) to enhance underwater image quality and feature visibility; though this comes at the expense of time and training. In this paper, we provide end-users with a simpler and faster interactive tone-mapping approach. This is built upon Weibull Tone Mapping (WTM) theory; introduced in previous work as a preferred tool to describe and improve domain expert TMOs. We allow end-users to easily shape brightness distributions according to the Weibull distribution, using two parameter sliders which modify the distribution peak and spread. Our experiments showed that 10 domain experts found the two-slider Weibull manipulation sufficed to make a desired adjustment in >80% of images in a large dataset. For the remaining ∼20%, observers opted for a control-point TMO which can, broadly, encompass many global tone mapping algorithms. Importantly, 91% of these control-point TMOs can actually be visually well-approximated by our Weibull slider manipulation, despite users not identifying slider parameters themselves. Our work stresses the benefit of the Weibull distribution and significance of image purpose in underwater image enhancement.

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“…On the other side, the UIQM combines image colorfulness, sharpness, and contrast [37]. Recall that these metrics are build based on subjective evaluation data [38].…”

Section: Classical Computer Vision Approachesmentioning

confidence: 99%

An Efficient Approach for Underwater Image Improvement: Deblurring, Dehazing, and Color Correction

Espinosa

McIntosh

Albu

2023

2023 IEEE/CVF Winter Conference on Applications of Computer Vision Workshops (WACVW)

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Large amounts of underwater imagery are constantly collected for environmental monitoring studies, as they are essential for estimating marine biodiversity and abundance. However, this collected data has variable quality due to uncontrolled environmental factors that cause blur and color casting. We attempt to address this issue by proposing two novel methods for underwater image enhancement.The first part of the thesis presents a deep learning architecture that integrates elements from classical methods to simultaneously address blurriness and color casting on underwater imagery in real time. We use two parallel architectures trained in a generative adversarial network scheme (GAN) with channel and spatial attention blocks to retrieve color, and discrete wavelength transform to preserve high-frequency components. Our experiments show that our method outperforms the state-of-the-art related works with respect to the structured similarity index metric (SSIM). Qualitative comparisons with color-checkers also show notable improvements over related works.The second part of the thesis proposes an unsupervised deep-learning approach for underwater image enhancement, which eliminates the need for reference images for training. This is an important step forward as for real (not synthetic) underwater images there is no high-quality reference available. Our method is based on a mathematical model for image dehazing. We use three networks to estimate the transmission map, the atmospheric light, and the enhanced image and propose a new compound loss function. We achieve results comparable to state-of-the-art supervised methods with respect to the SSIM while performing optimally at near real-time inference speeds.

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An Online Platform for Underwater Image Quality Evaluation

Cited by 4 publications

References 14 publications

Underwater image filtering: methods, datasets and evaluation

Underwater image filtering: methods, datasets and evaluation

Weibull Tone Mapping (WTM) for the Enhancement of Underwater Imagery

An Efficient Approach for Underwater Image Improvement: Deblurring, Dehazing, and Color Correction

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