Polarimetric Laser Range-Gated Underwater Imaging

For conventional laser range-gated underwater imaging （RG[） systems, the target image is obtained based oil the reflective character of the target. One of the main performance limiting factors of conventional RGI is that, when the underwater target has the same reflectivity as the background, it is difficult to distinguish the target from the background. An improvement is to use the polarization components of the reflected light. On the basis of conventional RGI, we propose a polarimetric RGI system that employs a polarization generator and a polarization analyzer to detect and recognize underwater objects. Experimental results demonstrate that, by combining polarization with intensity information, we are better able to enhance identification of the underwater target from other objects of the same reflectivity.

Restoring Polarization Angle Map for High- Fidelity Underwater Imaging

Obtaining polarization information enables researchers to enhance underwater imaging quality by removing backscattering effect and to distinguish targets of different materials.However,due to the simplified assumption of unpolarized target light,most of the existing underwater polari-metric methods lose part of the polarization information,resulting in degraded imaging quality.In this work,a novel underwater polarimetric method is reported,which obtains the angle of polariza-tion(AOP)map to improve imaging quality.Specifically,the Stokes vectors were exploited to re-move the backscattering effect by obtaining two pairs of orthogonal polarization sub-images of the underwater scene.The target reflected light and the angle between the polarization directions of the target reflected light and the backscattered light were computed through the two groups of the or-thogonal polarized sub-images.The AOP map of the target light could be derived from the Stokes vectors.Then,the transmission map of the target light was estimated by using the non-local color priorly combined with the properties of light propagating underwater.Experiments show that the reported technique enables distinguishing different targets when the colors are similar.The quantit-ative metrics validate that the reported technique produces state-of-the-art performance for under-water imaging.

Underwater Image Classification Based on EfficientnetB0 and Two-Hidden-Layer Random Vector Functional Link

The ocean plays an important role in maintaining the equilibrium of Earth’s ecology and providing humans access to a wealth of resources.To obtain a high-precision underwater image classification model,we propose a classification model that combines an EfficientnetB0 neural network and a two-hidden-layer random vector functional link network(EfficientnetB0-TRVFL).The features of underwater images were extracted using the EfficientnetB0 neural network pretrained via ImageNet,and a new fully connected layer was trained on the underwater image dataset using the transfer learning method.Transfer learning ensures the initial performance of the network and helps in the development of a high-precision classification model.Subsequently,a TRVFL was proposed to improve the classification property of the model.Net construction of the two hidden layers exhibited a high accuracy when the same hidden layer nodes were used.The parameters of the second hidden layer were obtained using a novel calculation method,which reduced the outcome error to improve the performance instability caused by the random generation of parameters of RVFL.Finally,the TRVFL classifier was used to classify features and obtain classification results.The proposed EfficientnetB0-TRVFL classification model achieved 87.28%,74.06%,and 99.59%accuracy on the MLC2008,MLC2009,and Fish-gres datasets,respectively.The best convolutional neural networks and existing methods were stacked up through box plots and Kolmogorov-Smirnov tests,respectively.The increases imply improved systematization properties in underwater image classification tasks.The image classification model offers important performance advantages and better stability compared with existing methods.

Unsupervised Multi-Expert Learning Model for Underwater Image Enhancement

Underwater image enhancement aims to restore a clean appearance and thus improves the quality of underwater degraded images.Current methods feed the whole image directly into the model for enhancement.However,they ignored that the R,G and B channels of underwater degraded images present varied degrees of degradation,due to the selective absorption for the light.To address this issue,we propose an unsupervised multi-expert learning model by considering the enhancement of each color channel.Specifically,an unsupervised architecture based on generative adversarial network is employed to alleviate the need for paired underwater images.Based on this,we design a generator,including a multi-expert encoder,a feature fusion module and a feature fusion-guided decoder,to generate the clear underwater image.Accordingly,a multi-expert discriminator is proposed to verify the authenticity of the R,G and B channels,respectively.In addition,content perceptual loss and edge loss are introduced into the loss function to further improve the content and details of the enhanced images.Extensive experiments on public datasets demonstrate that our method achieves more pleasing results in vision quality.Various metrics(PSNR,SSIM,UIQM and UCIQE) evaluated on our enhanced images have been improved obviously.

A Novel Multi-Stream Fusion Network for Underwater Image Enhancement

Due to the selective absorption of light and the existence of a large number of floating media in sea water, underwater images often suffer from color casts and detail blurs. It is therefore necessary to perform color correction and detail restoration. However,the existing enhancement algorithms cannot achieve the desired results. In order to solve the above problems, this paper proposes a multi-stream feature fusion network. First, an underwater image is preprocessed to obtain potential information from the illumination stream, color stream and structure stream by histogram equalization with contrast limitation, gamma correction and white balance, respectively. Next, these three streams and the original raw stream are sent to the residual blocks to extract the features. The features will be subsequently fused. It can enhance feature representation in underwater images. In the meantime, a composite loss function including three terms is used to ensure the quality of the enhanced image from the three aspects of color balance, structure preservation and image smoothness. Therefore, the enhanced image is more in line with human visual perception.Finally, the effectiveness of the proposed method is verified by comparison experiments with many stateof-the-art underwater image enhancement algorithms. Experimental results show that the proposed method provides superior results over them in terms of MSE,PSNR, SSIM, UIQM and UCIQE, and the enhanced images are more similar to their ground truth images.

MCRNet:Underwater image enhancement using multi-color space residual network

The selective attenuation and scattering of light in underwater environments cause color distortion and contrast reduction in underwater images,which can impede the ever-growing demand for underwater robot operations.To address these issues,we propose a Multi-Color space Residual Network(MCRNet)for underwater image enhancement.Our method takes advantage of the unique features of color representation in the RGB,HSV,and Lab color spaces.By utilizing the distinct feature representations of images in different color spaces,we can highlight and fuse the most informative features of the three color spaces.Our approach employs a self-attention mechanism in the multi-color space feature fusion module.Extensive experiments demonstrate that our method achieves satisfactory results in color correction and contrast improvement of underwater images,particularly in severely degraded scenes.Consequently,our method outperforms state-of-the-art methods in both subjective visual comparison and objective evaluation metrics.

Underwater Image Enhancement Based on Multi-scale Adversarial Network

In this study,an underwater image enhancement method based on multi-scale adversarial network was proposed to solve the problem of detail blur and color distortion in underwater images.Firstly,the local features of each layer were enhanced into the global features by the proposed residual dense block,which ensured that the generated images retain more details.Secondly,a multi-scale structure was adopted to extract multi-scale semantic features of the original images.Finally,the features obtained from the dual channels were fused by an adaptive fusion module to further optimize the features.The discriminant network adopted the structure of the Markov discriminator.In addition,by constructing mean square error,structural similarity,and perceived color loss function,the generated image is consistent with the reference image in structure,color,and content.The experimental results showed that the enhanced underwater image deblurring effect of the proposed algorithm was good and the problem of underwater image color bias was effectively improved.In both subjective and objective evaluation indexes,the experimental results of the proposed algorithm are better than those of the comparison algorithm.

UnderwaterWaste Recognition and Localization Based on Improved YOLOv5

With the continuous development of the economy and society,plastic pollution in rivers,lakes,oceans,and other bodies of water is increasingly severe,posing a serious challenge to underwater ecosystems.Effective cleaning up of underwater litter by robots relies on accurately identifying and locating the plastic waste.However,it often causes significant challenges such as noise interference,low contrast,and blurred textures in underwater optical images.A weighted fusion-based algorithm for enhancing the quality of underwater images is proposed,which combines weighted logarithmic transformations,adaptive gamma correction,improved multi-scale Retinex(MSR)algorithm,and the contrast limited adaptive histogram equalization(CLAHE)algorithm.The proposed algorithm improves brightness,contrast,and color recovery and enhances detail features resulting in better overall image quality.A network framework is proposed in this article based on the YOLOv5 model.MobileViT is used as the backbone of the network framework,detection layer is added to improve the detection capability for small targets,self-attention and mixed-attention modules are introduced to enhance the recognition capability of important features.The cross stage partial(CSP)structure is employed in the spatial pyramid pooling(SPP)section to enrich feature information,and the complete intersection over union(CIOU)loss is replaced with the focal efficient intersection over union(EIOU)loss to accelerate convergence while improving regression accuracy.Experimental results proved that the target recognition algorithm achieved a recognition accuracy of 0.913 and ensured a recognition speed of 45.56 fps/s.Subsequently,Using red,green,blue and depth(RGB-D)camera to construct a system for identifying and locating underwater plastic waste.Experiments were conducted underwater for recognition,localization,and error analysis.The experimental results demonstrate the effectiveness of the proposed method for identifying and locating underwater plastic waste,and it has good localization accuracy.

Underwater Image Enhancement Based on IMSRCR and CLAHE-WGIF

Aiming at the scattering and absorption of light in the water body,which causes the problems of color shift,uneven brightness,poor sharpness and missing details in the acquired underwater images,an underwater image enhancement algorithm based on IMSRCR and CLAHE-WGIF is proposed.Firstly,the IMSRCR algorithm proposed in this paper is used to process the original underwater image with adaptive color shift correction;secondly,the image is converted to HSV color space,and the segmentation exponential algorithm is used to process the S component to enhance the image saturation;finally,multi-scale Retinex is used to decompose the V component image into detail layer and base layer,and adaptive two-dimensional gamma correction is made to the base layer to adjust the brightness unevenness,while the detail layer is processed by CLAHE-WGIF algorithm to enhance the image contrast and detail information.The experimental results show that our algorithm has some advantages over existing algorithms in both subjective and objective evaluations,and the information entropy of the image is improved by 6.3%on average,and the UIQM and UCIQE indexes are improved by 12.9%and 20.3%on average.

Visual Enhancement of Underwater Images Using Transmission Estimation and Multi-Scale Fusion

The demand for the exploration of ocean resources is increasing exponentially.Underwater image data plays a significant role in many research areas.Despite this,the visual quality of underwater images is degraded because of two main factors namely,backscattering and attenuation.Therefore,visual enhancement has become an essential process to recover the required data from the images.Many algorithms had been proposed in a decade for improving the quality of images.This paper aims to propose a single image enhancement technique without the use of any external datasets.For that,the degraded images are subjected to two main processes namely,color correction and image fusion.Initially,veiling light and transmission light is estimated tofind the color required for correction.Veiling light refers to unwanted light,whereas transmission light refers to the required light for color correction.These estimated outputs are applied in the scene recovery equation.The image obtained from color correction is subjected to a fusion process where the image is categorized into two versions and applied to white balance and contrast enhancement techniques.The resultants are divided into three weight maps namely,luminance,saliency,chromaticity and fused using the Laplacian pyramid.The results obtained are graphically compared with their input data using RGB Histogram plot.Finally,image quality is measured and tabulated using underwater image quality measures.

Hydrophobic long-chain two-dimensional perovskite scintillators for underwater X-ray imaging

The underwater X-ray imaging technology development is significant to subaqueous target reconnaissance/detection/identification, subfluvial archaeology,submerged resource exploration, etc. As the core of X-ray imaging detection, the scintillator has been plagued by inherent moisture absorption and decomposition, and strict requirements for seamless packaging and waterproofing.Here, we designed a manganese-doped two-dimensional(2D) perovskite scintillator modified by hydrophobic longchain organic amine through the combination of component and doping engineering. The modified perovskites show high water repellency that can be used as an underwater X-ray scintillator. X-ray images of aquatic organisms or other objects with a high spatial resolution of10 lp·mm^(-1) at a big view field(32 mm × 32 mm) were obtained by scintillation screen. This hydrophobic perovskite scintillator based on molecular design is of great promise in underwater X-ray nondestructive testing technology development.

An Approach to Underwater Image Enhancement Based on Image Structural Decomposition

Underwater imaging posts a challenge due to the degradation by the absorption and scattering occurred during light propagation as well as poor lighting conditions in water medium Although image filtering techniques are utilized to improve image quality effectively, problems of the distortion of image details and the bias of color correction still exist in output images due to the complexity of image texture distribution. This paper proposes a new underwater image enhancement method based on image struc- tural decomposition. By introducing a curvature factor into the Mumford_Shah_G decomposition algorithm, image details and struc- ture components are better preserved without the gradient effect. Thus, histogram equalization and Retinex algorithms are applied in the decomposed structure component for global image enhancement and non-uniform brightness correction for gray level and the color images, then the optical absorption spectrum in water medium is incorporate to improve the color correction. Finally, the en- hauced structure and preserved detail component are re.composed to generate the output. Experiments with real underwater images verify the image improvement by the proposed method in image contrast, brightness and color fidelity.

A Fast Underwater Optical Image Segmentation Algorithm Based on a Histogram Weighted Fuzzy C-means Improved by PSO

The S/N of an underwater image is low and has a fuzzy edge.If using traditional methods to process it directly,the result is not satisfying.Though the traditional fuzzy C-means algorithm can sometimes divide the image into object and background,its time-consuming computation is often an obstacle.The mission of the vision system of an autonomous underwater vehicle (AUV) is to rapidly and exactly deal with the information about the object in a complex environment for the AUV to use the obtained result to execute the next task.So,by using the statistical characteristics of the gray image histogram,a fast and effective fuzzy C-means underwater image segmentation algorithm was presented.With the weighted histogram modifying the fuzzy membership,the above algorithm can not only cut down on a large amount of data processing and storage during the computation process compared with the traditional algorithm,so as to speed up the efficiency of the segmentation,but also improve the quality of underwater image segmentation.Finally,particle swarm optimization (PSO) described by the sine function was introduced to the algorithm mentioned above.It made up for the shortcomings that the FCM algorithm can not get the global optimal solution.Thus,on the one hand,it considers the global impact and achieves the local optimal solution,and on the other hand,further greatly increases the computing speed.Experimental results indicate that the novel algorithm can reach a better segmentation quality and the processing time of each image is reduced.They enhance efficiency and satisfy the requirements of a highly effective,real-time AUV.

Underwater Image Bidirectional Matching for Localization Based on SIFT

For the purpose of identifying the stern of the SWATH （Small Waterplane Area Twin Hull） availably and perfecting the detection technique of the SWATH ship＇s performance, this paper presents a novel bidirectional image registration strategy and mosaicing technique based on the scale invariant feature transform （SIFT） algorithm. The proposed method can help us observe the stern with a great visual angle for analyzing the performance of the control fins of the SWATH. SIFT is one of the most effective local features of the scale, rotation and illumination invariant. However, there are a few false match rates in this algorithm. In terms of underwater machine vision, only by acquiring an accurate match rate can we find an underwater robot rapidly and identify the location of the object. Therefore, firstly, the selection of the match ratio principle is put forward in this paper; secondly, some advantages of the bidirectional registration algorithm are concluded by analyzing the characteristics of the unidirectional matching method. Finally, an automatic underwater image splicing method is proposed on the basis of fixed dimension, and then the edge of the image＇s overlapping section is merged by the principal components analysis algorithm. The experimental results achieve a better registration and smooth mosaicing effect, demonstrating that the proposed method is effective.

Research on the Application of Super Resolution Reconstruction Algorithm for Underwater Image

Underwater imaging is widely used in ocean,river and lake exploration,but it is affected by properties of water and the optics.In order to solve the lower-resolution underwater image formed by the influence of water and light,the image super-resolution reconstruction technique is applied to the underwater image processing.This paper addresses the problem of generating super-resolution underwater images by convolutional neural network framework technology.We research the degradation model of underwater images,and analyze the lower-resolution factors of underwater images in different situations,and compare different traditional super-resolution image reconstruction algorithms.We further show that the algorithm of super-resolution using deep convolution networks(SRCNN)which applied to super-resolution underwater images achieves good results.

Deep Neural Network Driven Automated Underwater Object Detection

Object recognition and computer vision techniques for automated object identification are attracting marine biologist’s interest as a quicker and easier tool for estimating the fish abundance in marine environments.However,the biggest problem posed by unrestricted aquatic imaging is low luminance,turbidity,background ambiguity,and context camouflage,which make traditional approaches rely on their efficiency due to inaccurate detection or elevated false-positive rates.To address these challenges,we suggest a systemic approach to merge visual features and Gaussian mixture models with You Only Look Once(YOLOv3)deep network,a coherent strategy for recognizing fish in challenging underwater images.As an image restoration phase,pre-processing based on diffraction correction is primarily applied to frames.The YOLOv3 based object recognition system is used to identify fish occurrences.The objects in the background that are camouflaged are often overlooked by the YOLOv3 model.A proposed Bi-dimensional Empirical Mode Decomposition(BEMD)algorithm,adapted by Gaussian mixture models,and integrating the results of YOLOv3 improves detection efficiency of the proposed automated underwater object detection method.The proposed approach was tested on four challenging video datasets,the Life Cross Language Evaluation Forum(CLEF)benchmark from the F4K data repository,the University of Western Australia(UWA)dataset,the bubble vision dataset and theDeepFish dataset.The accuracy for fish identification is 98.5 percent,96.77 percent,97.99 percent and 95.3 percent respectively for the various datasets which demonstrate the feasibility of our proposed automated underwater object detection method.

Underwater Terrain Image Stitching Based on Spatial Gradient Feature Block

At present,underwater terrain images are all strip-shaped small fragment images preprocessed by the side-scan sonar imaging system.However,the processed underwater terrain images have inconspicuous and few feature points.In order to better realize the stitching of underwater terrain images and solve the problems of slow traditional image stitching speed,we proposed an improved algorithm for underwater terrain image stitching based on spatial gradient feature block.First,the spatial gradient fuzzy C-Means algorithm is used to divide the underwater terrain image into feature blocks with the fusion of spatial gradient information.The accelerated-KAZE(AKAZE)algorithm is used to combine the feature block information to match the reference image and the target image.Then,the random sample consensus(RANSAC)is applied to optimize the matching results.Finally,image fusion is performed with the global homography and the optimal seam-line method to improve the accuracy of image overlay fusion.The experimental results show that the proposed method in this paper effectively divides images into feature blocks by combining spatial information and gradient information,which not only solves the problem of stitching failure of underwater terrain images due to unobvious features,and further reduces the sensitivity to noise,but also effectively reduces the iterative calculation in the feature point matching process of the traditional method,and improves the stitching speed.Ghosting and shape warping are significantly eliminated by re-optimizing the overlap of the image.

An Approach to Synthesize Diverse Underwater Image Dataset

Images that are taken underwater mostly present color shift with hazy effects due to the special property of water.Underwater image enhancement methods are proposed to handle this issue.However,their enhancement results are only evaluated on a small number of underwater images.The lack of a sufficiently large and diverse dataset for efficient evaluation of underwater image enhancement methods provokes the present paper.The present paper proposes an organized method to synthesize diverse underwater images,which can function as a benchmark dataset.The present synthesis is based on the underwater image formation model,which describes the physical degradation process.The indoor RGB-D image dataset is used as the seed for underwater style image generation.The ambient light is simulated based on the statistical mean value of real-world underwater images.Attenuation coefficients for diverse water types are carefully selected.Finally,in total 14490 underwater images of 10 water types are synthesized.Based on the synthesized database,state-of-the-art image enhancement methods are appropriately evaluated.Besides,the large diverse underwater image database is beneficial in the development of learning-based methods.

DESIGN OF MODULATION AND COMPRESSION CODING IN UNDERWATER ACOUSTIC IMAGE TRANSMISSION

This paper describes the design of modulation, compression coding and transmission control in underwater acoustic color image transmission system. This design adapts a special system of modulation and transmission control based on a DSP(Digital Signal Processing) chip, to cope with the complex underwater acoustic channel. The hardware block diagram and software flow chart are presented.

Image Quality Improvement for Underwater Visual Inspections of Nuclear Power Plants

Visual inspection of the key components of nuclear power plants(NPPs)is important for NPP operation and maintenance. However,the underwater environment and existing radiation will lead to image degradation,thus making it difficult to identify surface defects. In this study,a method for improving the quality of underwater images is proposed.By analyzing the degradation characteristics of underwater detection image,the image enhancement technology is used to improve the color richness of the image,and then the improved dark channel prior(DCP)algorithm is used to restore it. By modifying the estimation formula of transmittance and background light,the correction of insufficient brightness in DCP restored image is realized. The proposed method is compared with other state-of-the-art methods. The results show that the proposed method can achieve higher scores and improve the image quality by correcting the color and restoring local details,thus effectively enhancing the reliability of visual inspection of NPPs.