UWStereoNet: Unsupervised Learning for Depth Estimation and Color Correction of Underwater Stereo Imagery

Skinner, Katherine A.; Zhang, Junming; Olson, Elizabeth A.; Johnson-Roberson, Matthew

doi:10.1109/icra.2019.8794272

Cited by 30 publications

(15 citation statements)

References 25 publications

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“…In [49], they use the Jaffe-McGlamery model [50,51], a mathematical method, to handle the problems, which decreases the absorption and scattering effects based on irradiance and depth. In [52], a learning-based method was proposed to solve depth estimation and color correction in spherical domains at the same time by solving left-right consistency under a multicamera setting. However, deep learning usually requires a large amount of data, which is not available for the underwater field.…”

Section: Underwater Depth Estimation and Color Correctionmentioning

confidence: 99%

Underwater Depth Estimation for Spherical Images

Cui

Jin

Kuang

et al. 2021

Journal of Robotics

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This paper proposes a method for monocular underwater depth estimation, which is an open problem in robotics and computer vision. To this end, we leverage publicly available in-air RGB-D image pairs for underwater depth estimation in the spherical domain with an unsupervised approach. For this, the in-air images are style-transferred to the underwater style as the first step. Given those synthetic underwater images and their ground truth depth, we then train a network to estimate the depth. This way, our learning model is designed to obtain the depth up to scale, without the need of corresponding ground truth underwater depth data, which is typically not available. We test our approach on style-transferred in-air images as well as on our own real underwater dataset, for which we computed sparse ground truth depths data via stereopsis. This dataset is provided for download. Experiments with this data against a state-of-the-art in-air network as well as different artificial inputs show that the style transfer as well as the depth estimation exhibit promising performance.

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Section: Underwater Depth Estimation and Color Correctionmentioning

confidence: 99%

Underwater Depth Estimation for Spherical Images

Cui

Jin

Kuang

et al. 2021

Journal of Robotics

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“…This dataset contains 37 challenging stereo underwater images, which were selected from the coral site images of HIMB#1 dataset [55].…”

Section: Hawaii Institute Of Marine Biology (Himb) Databasementioning

confidence: 99%

Single Underwater Image Restoration Based on Adaptive Transmission Fusion

Chang

2020

IEEE Access

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Underwater images are often deteriorated with blurring, darkness, poor visual quality of low contrast, and color diminishing. This is mainly due to the fact that the light is exponentially attenuated while traveling through water and the strength of attenuation is color dependent. After constructing a simplified image formation model, this paper proposes a new strategy for single underwater image restoration. In light of different perspectives, two distinct transmission coefficient estimation approaches have been developed. One is based on the optical characteristics while the other relies on the essence of image processing knowledge. Subsequently, these two transmission maps are fused to produce the final outcome, which is adaptively weighted by their respective saliency maps. The obtained signal radiance is dissolved through point spread function deconvolution and color compensation to produce the final scene radiance. A variety of underwater images with various scenarios were exploited to evaluate the restoration performance. Experimental results demonstrated the superiority of the proposed algorithm over other competitive methods for underwater image restoration. INDEX TERMS Image restoration, transmission fusion, saliency map, dark channel prior, blurriness detection.

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“…At present, many researchers are attempting to synthesize underwater images with in-air RGB-D images to build paired datasets for underwater image color restoration [ 16 , 17 , 18 ] or depth map estimation [ 10 , 11 , 19 ]. For instance, WaterGAN [ 16 ] and UWGAN [ 20 ] input a paired in-air RGB-D image into a physical-model-based generator such that the final output is a synthesized underwater image produced by the generator [ 10 , 11 ].…”

Section: Introductionmentioning

confidence: 99%

“… To enrich our synthesized underwater dataset, we propose a disentangled representation loss along with style diversification loss to identify interpretable and meaningful representations from the unlabeled underwater dataset and the synthesized underwater images with a rich diversity. Following the coarse-to-fine principle, and inspired by the work of Eigen et al [ 23 ] and Skinner et al [ 19 ], our approach adopted global–local generators for the estimation of coarse and fine depth maps, respectively. We evaluated our model on a real underwater RGB-D dataset and achieved better results than those of other state-of-the-art models.…”

Section: Introductionmentioning

confidence: 99%

“…Following the coarse-to-fine principle, and inspired by the work of Eigen et al [ 23 ] and Skinner et al [ 19 ], our approach adopted global–local generators for the estimation of coarse and fine depth maps, respectively. We evaluated our model on a real underwater RGB-D dataset and achieved better results than those of other state-of-the-art models.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Unpaired Underwater Image Synthesis with a Disentangled Representation for Underwater Depth Map Prediction

Zhao

Zhou

et al. 2021

Sensors

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As one of the key requirements for underwater exploration, underwater depth map estimation is of great importance in underwater vision research. Although significant progress has been achieved in the fields of image-to-image translation and depth map estimation, a gap between normal depth map estimation and underwater depth map estimation still remains. Additionally, it is a great challenge to build a mapping function that converts a single underwater image into an underwater depth map due to the lack of paired data. Moreover, the ever-changing underwater environment further intensifies the difficulty of finding an optimal mapping solution. To eliminate these bottlenecks, we developed a novel image-to-image framework for underwater image synthesis and depth map estimation in underwater conditions. For the problem of the lack of paired data, by translating hazy in-air images (with a depth map) into underwater images, we initially obtained a paired dataset of underwater images and corresponding depth maps. To enrich our synthesized underwater dataset, we further translated hazy in-air images into a series of continuously changing underwater images with a specified style. For the depth map estimation, we included a coarse-to-fine network to provide a precise depth map estimation result. We evaluated the efficiency of our framework for a real underwater RGB-D dataset. The experimental results show that our method can provide a diversity of underwater images and the best depth map estimation precision.

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UWStereoNet: Unsupervised Learning for Depth Estimation and Color Correction of Underwater Stereo Imagery

Cited by 30 publications

References 25 publications

Underwater Depth Estimation for Spherical Images

Underwater Depth Estimation for Spherical Images

Single Underwater Image Restoration Based on Adaptive Transmission Fusion

Unpaired Underwater Image Synthesis with a Disentangled Representation for Underwater Depth Map Prediction

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