The Next Best Underwater View

Sheinin, Mark; Schechner, Yoav Y.

doi:10.1109/cvpr.2016.409

Cited by 46 publications

(21 citation statements)

References 50 publications

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“…Yau et al [25] extended the existing works on physical refraction models by considering the dispersion of light, and derived new constraints on the model parameters for underwater camera calibration. Sheinin et al [26] generalized the next best view concept of robot vision to scattering media and cooperative movable lighting for underwater navigation. Akkaynak et al [27] introduced the space of attenuation coefficients that can be used for many underwater computer vision tasks.…”

Section: Related Workmentioning

confidence: 99%

Effects of Image Degradation and Degradation Removal to CNN-Based Image Classification

Pei

Huang

Zou

et al. 2021

IEEE Trans. Pattern Anal. Mach. Intell.

192

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Just like many other topics in computer vision, image classification has achieved significant progress recently by using deep-learning neural networks, especially the Convolutional Neural Networks (CNN). Most of the existing works are focused on classifying very clear natural images, evidenced by the widely used image databases such as Caltech-256, PASCAL VOCs and ImageNet. However, in many real applications, the acquired images may contain certain degradations that lead to various kinds of blurring, noise, and distortions. One important and interesting problem is the effect of such degradations to the performance of CNN-based image classification. More specifically, we wonder whether image-classification performance drops with each kind of degradation, whether this drop can be avoided by including degraded images into training, and whether existing computer vision algorithms that attempt to remove such degradations can help improve the image-classification performance. In this paper, we empirically study this problem for four kinds of degraded images -hazy images, underwater images, motion-blurred images and fish-eye images. For this study, we synthesize a large number of such degraded images by applying respective physical models to the clear natural images and collect a new hazy image dataset from the Internet. We expect this work can draw more interests from the community to study the classification of degraded images.

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Section: Related Workmentioning

confidence: 99%

Effects of Image Degradation and Degradation Removal to CNN-Based Image Classification

Pei

Huang

Zou

et al. 2021

IEEE Trans. Pattern Anal. Mach. Intell.

192

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show abstract

“…D URING the past few years, underwater image enhancement has drawn considerable attention in both image processing and underwater vision [1], [2]. Due to the complicated underwater environment and lighting conditions, enhancing underwater image is a challenging problem.…”

Section: Introductionmentioning

confidence: 99%

An Underwater Image Enhancement Benchmark Dataset and Beyond

Guo

Ren

et al. 2020

IEEE Trans. on Image Process.

1,245

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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“…However this method requires an active illumination system and is valid only for short distances, since near-infrared light is rapidly attenuated in water. Known geometry is used in [31] to plan the imaging viewpoints under water that result in high contrast.…”

Section: Related Workmentioning

confidence: 99%

Color Restoration of Underwater Images

Menaker¹,

Treibitz

Avidan

2017

Procedings of the British Machine Vision Conference 2017

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Underwater images suffer from color distortion and low contrast, because light is attenuated as it propagates through water. The attenuation varies with wavelength and depends both on the properties of the water body in which the image was taken and the 3D structure of the scene, making it difficult to restore the colors. Existing single underwater image enhancement techniques either ignore the wavelength dependency of the attenuation, or assume a specific spectral profile. We propose a new method that takes into account multiple spectral profiles of different water types, and restores underwater scenes from a single image.We show that by estimating just two additional global parameters -the attenuation ratios of the blue-red and blue-green color channels -the problem of underwater image restoration can be reduced to single image dehazing, where all color channels have the same attenuation coefficients. Since we do not know the water type ahead of time, we try different parameter sets out of an existing library of water types. Each set leads to a different restored image and the one that best satisfies the Gray-World assumption is chosen. The proposed single underwater image restoration method is fully automatic and is based on a more comprehensive physical image formation model than previously used.We collected a dataset of real images taken in different locations with varying water properties and placed color charts in the scenes. Moreover, to obtain ground truth, the 3D structure of the scene was calculated based on stereo imaging. This dataset enables a quantitative evaluation of restoration algorithms on natural images and shows the advantage of the proposed method.

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The Next Best Underwater View

Cited by 46 publications

References 50 publications

Effects of Image Degradation and Degradation Removal to CNN-Based Image Classification

Effects of Image Degradation and Degradation Removal to CNN-Based Image Classification

An Underwater Image Enhancement Benchmark Dataset and Beyond

Color Restoration of Underwater Images

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