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.

scholarly journals Role of Restored Underwater Images in Underwater Imaging Applications

2021 ◽  
Vol 4 (4) ◽  
pp. 96
Author(s):  
Jarina Raihan A ◽  
Pg Emeroylariffion Abas ◽  
Liyanage C De Silva
Keyword(s):  
Machine Learning ◽  
Image Classification ◽  
Feature Detection ◽  
Sea Water ◽  
Learning Algorithm ◽  
Underwater Imaging ◽  
Underwater Image ◽  
Common Problems ◽  
Restoration Method

Underwater images are extremely sensitive to distortion occurring in an aquatic underwater environment, with absorption, scattering, polarization, diffraction and low natural light penetration representing common problems caused by sea water. Because of these degradation of quality, effectiveness of the acquired images for underwater applications may be limited. An effective method of restoring underwater images has been demonstrated, by considering the wavelengths of red, blue, and green lights, attenuation and backscattering coefficients. The results from the underwater restoration method have been applied to various underwater applications; particularly, edge detection, Speeded Up Robust Feature detection, and image classification that uses machine learning. It has been shown that more edges and more SURF points can be detected as a result of using the method. Applying the method to restore underwater images in image classification tasks on underwater image datasets gives accuracy of up to 89% using a simple machine-learning algorithm. These results are significant as it demonstrates that the restoration method can be implemented on underwater system for various purposes.

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

Benthic Habitats and the Effects of Fishing

2005 ◽  
Keyword(s):  
Scattered Light ◽  
Fish Habitat ◽  
Laser Line ◽  
Underwater Imaging ◽  
Benthic Habitats ◽  
Sea Floor ◽  
Line Scan ◽  
Imaging Sensors ◽  
Laser Line Scan

Laser-based underwater imaging sensors have been developed and matured in the last decade that provide high resolution optical imagery of the sea floor. Laser Line Scan (LLS) and Streak Tube Imaging Lidar (STIL) have been particularly successful. A prototype Fluorescence Imaging Laser Line Scan (FILLS) sensor has been deployed in several underwater environments, yielding highresolution (~1 cm pixel size) imagery of the associated benthic habitats. The prototype FILLS sensor illuminates the sea floor with 488 nm laser light, and constructs four independent images from light collected at 488 nm, 520 nm, 580 nm, and 685 nm, respectively. The 488 nm image is formed from elastically scattered light (i.e., light scattered with no change in photon energy), while the other images are formed by inelastically scattered light. (The FILLS sensor is routinely operated during nighttime hours so that ambient illumination is negligible). Fluorescence is the primary physical mechanism giving rise to the inelastically scattered light sensed by FILLS. Coral reef environments produce particularly strong (and spectacular!) fluorescence imagery. FILLS was developed primarily for the detection, classification, and identification of man-made objects in underwater environments. In addition it can serve admirably for the characterization of underwater habitats. Examples of FILLS imagery relevant to fish habitat evaluation will be presented.

Magneto-Optic Spectroscopy on Neon Using Laser-Induced Forward Scattering

1994 ◽  
Vol 48 (12) ◽  
pp. 1532-1538 ◽  
Author(s):  
Åsa Lindberg
Keyword(s):  
Scattered Light ◽  
Single Mode ◽  
Forward Scattering ◽  
Spatial Effects ◽  
Line Shapes ◽  
Calculated Line ◽  
Scattering Spectra ◽  
Light Matter Interaction ◽  
Isotopic Effects ◽  
Atomic Parameters

The forward-scattered light observed when a single-mode dye laser interacts with the three neon transitions 1 s5( J = 2)-2 ps( J = 2), 1s3( J = 0)-2 p5( J = 1), and 1 s4( J = 1)-2 p7( J = 1) has been investigated as an applied longitudinal magnetic field is varied. The 1 s5 and the 1 s3 states are metastable. This condition allows a comparison of the spectra for the cases 1 s5( J = 2)-2 p8( J = 2) and 1 s3( J = 0)-2 p5( J = 1) in order to discuss whether the complex line shapes obtained for the J = 2 to J = 2 atomic system are due to higher-order coherences or whether they originate from coherences between m = ±1 states. Experimental and calculated line shapes are presented. The calculations have been performed with the use of a semiclassical model for the light/matter interaction. A J = 0 to J = 1 (or a J = 1 to J = 1) model reproduces the overall shapes of the recorded spectra for all three transitions. The results show that, even when laser beam spatial effects and the sample isotopic effects are taken into account, interpretations of forward-scattering spectra in order to determine atomic parameters can reliably be done for simple J = 0 to J = 1 systems only.

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

Leveraging Deep Statistics for Underwater Image Enhancement

2021 ◽  
Vol 17 (3s) ◽  
pp. 1-20
Author(s):  
Yang Wang ◽  
Yang Cao ◽  
Jing Zhang ◽  
Feng Wu ◽  
Zheng-Jun Zha
Keyword(s):  
Contrast Enhancement ◽  
Image Enhancement ◽  
Light Propagation ◽  
Synthesis Method ◽  
Color Correction ◽  
Training Data ◽  
Depth Of Field ◽  
Underwater Imaging ◽  
Underwater Image ◽  
Color Cast

Underwater imaging often suffers from color cast and contrast degradation due to range-dependent medium absorption and light scattering. Introducing image statistics as prior has been proved to be an effective solution for underwater image enhancement. However, relative to the modal divergence of light propagation and underwater scenery, the existing methods are limited in representing the inherent statistics of underwater images resulting in color artifacts and haze residuals. To address this problem, this article proposes a convolutional neural network (CNN)-based framework to learn hierarchical statistical features related to color cast and contrast degradation and to leverage them for underwater image enhancement. Specifically, a pixel disruption strategy is first proposed to suppress intrinsic colors’ influence and facilitate modeling a unified statistical representation of underwater image. Then, considering the local variation of depth of field, two parallel sub-networks: Color Correction Network (CC-Net) and Contrast Enhancement Network (CE-Net) are presented. The CC-Net and CE-Net can generate pixel-wise color cast and transmission map and achieve spatial-varied color correction and contrast enhancement. Moreover, to address the issue of insufficient training data, an imaging model-based synthesis method that incorporates pixel disruption strategy is presented to generate underwater patches with global degradation consistency. Quantitative and subjective evaluations demonstrate that our proposed method achieves state-of-the-art performance.

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
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