Underwater image recovery method based on hyperspectral polarization imaging

2021 ◽  
Vol 484 ◽  
pp. 126691
Author(s):  
Jiamin Qian ◽  
Jianxin Li ◽  
Yubo Wang ◽  
Jie Liu ◽  
Jiaxin Wang ◽  
...  
Keyword(s):  
Image Recovery ◽  
Polarization Imaging ◽  
Recovery Method ◽  
Underwater Image

scholarly journals Underwater Image Recovery Under the Nonuniform Optical Field Based on Polarimetric Imaging

2018 ◽  
Vol 10 (1) ◽  
pp. 1-9 ◽  
Author(s):  
Haofeng Hu ◽  
Lin Zhao ◽  
Xiaobo Li ◽  
Hui Wang ◽  
Tiegen Liu
Keyword(s):  
Optical Field ◽  
Image Recovery ◽  
Underwater Image ◽  
Polarimetric Imaging

scholarly journals Underwater 2D Image Acquisition Using Sequential Striping Illumination

2019 ◽  
Vol 9 (11) ◽  
pp. 2179
Author(s):  
Benxing Gong ◽  
Guoyu Wang
Keyword(s):  
Image Acquisition ◽  
Scattered Light ◽  
Forward Scattering ◽  
Image Recovery ◽  
Underwater Imaging ◽  
Scattering Problems ◽  
Structured Lighting ◽  
Underwater Image ◽  
Scattering Effects ◽  
Scanning Systems

Structured lighting techniques have increasingly been employed in underwater imaging, where scattering effects cannot be ignored. This paper presents an approach to underwater image recovery using structured light as a scanning mode. The method tackles both the forward scattering and back scattering problems. By integrating each of the sequentially striping illuminated frame images, we generate a synthesized image that can be modeled on the convolution of the surface albedo and the illumination function. Thus, image acquisition is issued as a problem of image recovery by deconvolution. The convolutional model has the advantage of integrating the forward scattering light into a recovered image so as to eliminate image blur. Notably, the removal of the back scattered light from each frame image can be easily realized by a virtual aperture to limit the field of view; the same principle as of the synchronous scanning systems in underwater imaging. Herein, the implementation of the proposed approach is described, and the results of the underwater experiments are presented.

Adaptive underwater image recovery using polarimetric imaging

2019 ◽  
Author(s):  
Zheng Liang ◽  
Congcong Zhao ◽  
Yafei Wang ◽  
Xueyan Ding ◽  
Zetian Mi ◽  
...  
Keyword(s):  
Image Recovery ◽  
Underwater Image ◽  
Polarimetric Imaging

A novel image recovery method based on discrete cosine transform and matched blocks

2013 ◽  
Vol 73 (3) ◽  
pp. 1945-1954 ◽  
Author(s):  
Xingyuan Wang ◽  
Dandan Zhang ◽  
Xing Guo
Keyword(s):  
Discrete Cosine Transform ◽  
Image Recovery ◽  
Recovery Method ◽  
Cosine Transform

Polarimetric underwater image recovery via deep learning

2020 ◽  
Vol 133 ◽  
pp. 106152
Author(s):  
Haofeng Hu ◽  
Yanbin Zhang ◽  
Xiaobo Li ◽  
Yang Lin ◽  
Zhenzhou Cheng ◽  
...  
Keyword(s):  
Deep Learning ◽  
Image Recovery ◽  
Underwater Image

scholarly journals Underwater image recovery considering polarization effects of objects

2016 ◽  
Vol 24 (9) ◽  
pp. 9826 ◽  
Author(s):  
Bingjing Huang ◽  
Tiegen Liu ◽  
Haofeng Hu ◽  
Jiahui Han ◽  
Mingxuan Yu
Keyword(s):  
Image Recovery ◽  
Polarization Effects ◽  
Underwater Image

scholarly journals UNDERWATER IMAGE ENHANCEMENT BASED ON POLARIZATION IMAGING

2020 ◽  
Vol XLIII-B1-2020 ◽  
pp. 579-585
Author(s):  
L. Shen ◽  
Y. Zhao
Keyword(s):  
Ecological Research ◽  
Underwater Imaging ◽  
High Quality ◽  
Polarization Imaging ◽  
Detection And Tracking ◽  
Imaging Model ◽  
Underwater Image ◽  
Underwater Object ◽  
Image Enhancing ◽  
The Relationship

Abstract. The need of high-quality underwater imaging is obviously required in many underwater applications. For example, underwater archaeology, underwater ecological research, underwater object detection and tracking. This paper presents a joint enhancing and denoising scheme for an image taken in underwater conditions. Conventional image enhancing methods may amplify the noise depending on the distance and density of the particles in the water. To suppress the noise and improve the enhancement performance, an imaging model is modified by adding the process of amplifying the noise in underwater conditions. This model offers depth-chromaticity compensation regularization for the transmission map and chromaticity-depth compensation regularization for enhancing the image. The proposed iterative underwater image enhancing method with polarization uses these two joint regularization schemes and the relationship between the transmission map and enhanced irradiance image. The transmission map and irradiance image are used to promote each other. To verify the effectiveness of the algorithm, polarizing images of different scenes in different conditions are collected. Different algorithms are applied to the original images. Experimental results demonstrate that the proposed scheme increases visibility in extreme conditions without amplifying the noise.

Underwater image recovery utilizing polarimetric imaging based on neural networks

2021 ◽  
Vol 60 (27) ◽  
pp. 8419
Author(s):  
Ran Zhang ◽  
Xinyuan Gui ◽  
Haoyuan Cheng ◽  
Jinkui Chu
Keyword(s):  
Neural Networks ◽  
Image Recovery ◽  
Underwater Image ◽  
Polarimetric Imaging

scholarly journals Underwater Image Recovery Using Structured Light

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 77183-77189 ◽  
Author(s):  
Gong Benxing ◽  
Guoyu Wang
Keyword(s):  
Structured Light ◽  
Image Recovery ◽  
Underwater Image
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