134 results about "Underwater imaging" patented technology

The invention discloses an end-to-end underwater three-dimensional reconstruction method and system based on an underwater imaging model, and belongs to the technical field of underwater image processing. According to the method, deep learning pose estimation is combined with an underwater imaging model; firstly, a multi-frame underwater image sequence is collected as input, a deep neural network is constructed, and the network mainly comprises two core sub-modules: a pose estimation network; secondly, a three-dimensional reconstruction network is adopted, dense point cloud or voxel reconstruction is completed according to the predicted pose and image content, pose estimation and the three-dimensional reconstruction process are integrated in the same system, and overall joint optimization is achieved; and in combination with a self-adaptive underwater imaging model, modeling is performed on physical processes such as underwater illumination attenuation and scattering, so that the reality sense and the accuracy of a reconstruction result are improved. According to the method, high-quality three-dimensional reconstruction of images in a complex underwater environment is realized, and the method can be widely applied to ocean engineering, underwater robots, submarine topography surveying and mapping and underwater cultural relic protection.

The invention discloses an underwater three-dimensional scene reconstruction method and device based on a three-dimensional Gaussian splashing and underwater imaging model. The method comprises the following steps: constructing an underwater truth value image, corresponding camera parameters and sparse initialization point cloud into a data set; training a first underwater radiation field based on the three-dimensional Gaussian splash model and the underwater imaging model by using the data set and the three-dimensional reconstruction loss function, and training a second underwater radiation field based on the three-dimensional Gaussian splash model and the underwater imaging model by using the data set, the three-dimensional reconstruction loss function and the self-trimming supervision loss function; and generating a water-containing underwater image under the new visual angle by using the trained three-dimensional Gaussian splash model in the first underwater radiation field and the underwater imaging model, and generating a water-free underwater image under the new visual angle by using the trained three-dimensional Gaussian splash model in the second underwater radiation field. The method has the advantages that the training speed is high, the image rendering and three-dimensional reconstruction quality is high, and the underwater image with water or without water can be selectively rendered.

The invention relates to the technical field of underwater imaging, in particular to an underwater multispectral ghost imaging system based on deep learning image super-resolution reconstruction, which comprises a light modulation module, a multispectral light intensity data acquisition module and a control and image reconstruction module, the control and image reconstruction module is respectively connected with the light modulation module and the multispectral light intensity data acquisition module, the control and image reconstruction module controls the light modulation module to modulate an illumination light beam and then project the illumination light beam to a target object, and the modulated illumination light beam is reflected by the target object and then received by the multispectral light intensity data acquisition module; and the multispectral light intensity data acquisition module sends the received reflected light beam to the control and image reconstruction module, image reconstruction is performed through a pre-trained deep neural network in the control and image reconstruction module, and a super-resolution image is output. The underwater multispectral ghost imaging system is provided in combination with a deep learning image super-resolution reconstruction algorithm, and the imaging quality and resolution are improved.

The invention relates to the technical field of ocean engineering, in particular to an underwater imaging system for offshore wind power construction, which comprises a turbidity detection unit, a judgment unit, an acquisition unit and a processing unit, the turbidity detection unit is used for detecting and calculating the turbidity R of the water body in the construction area; the judgment unit is used for judging the turbidity condition of the water body in the construction area based on the turbidity R grade and sending different acquisition signals; the acquisition unit comprises an optical subunit, a sonar subunit and a fluorescence subunit; the processing unit is used for carrying out underwater imaging on the optical subunit, the sonar subunit or the fluorescence subunit correspondingly started according to the received different acquisition signals and sending signals. The turbidity of a water body in a construction area is detected, and the turbidity conditions are divided into light turbidity, medium turbidity and heavy turbidity based on the turbidity; different imaging technologies are combined with different turbid conditions, so that the underwater imaging quality in offshore wind power construction is improved, and the construction safety is improved.

The invention discloses an underwater image enhancement method and system based on binocular vision and polarization imaging. The method comprises the following steps: firstly, synchronously acquiring an orthogonal polarization image pair through a binocular polarization imaging unit, recovering scene depth information from the polarization image pair by using a binocular stereoscopic vision technology, and calculating a transmissivity graph; analyzing the polarization characteristic difference between target reflected light and backscattered light, establishing a polarization difference model, and separating background scattered light; solving an underwater imaging equation in combination with the depth information and a polarization difference model to obtain a preliminary restoration result of the target reflected light; and finally, further improving the image quality through the steps of multi-scale spectrum analysis, adaptive filter construction and post-processing by adopting a spectrum adaptive image enhancement technology. The system comprises a binocular polarization imaging unit, a calculation processing unit and an active illumination unit. According to the method, the problems of color distortion, low contrast and fuzzy details of the underwater image are solved, and the visual quality and usability of the underwater image are improved.

The invention belongs to the technical field of image processing, and discloses an underwater image quality evaluation method based on sparse representation and Transform, comprising the following steps: S1, selecting N underwater images with subjective quality labels, and extracting visual perception features of each underwater image by using sparse representation; s2, extracting a semantic feature of each underwater image by using a Transform; s3, splicing the visual perception features and the semantic features, inputting the spliced features into a multi-layer perceptron, and constructing an underwater image quality evaluation model; s4, performing end-to-end training on the underwater image quality evaluation model; and S5, predicting the quality of an underwater image to be evaluated by using the trained image quality model. The method does not need to refer to an original image, can obtain high prediction performance, and meets the application requirements of underwater imaging.

The invention provides a method for repairing an underwater degraded image. The method comprises the following steps: S1, inputting the underwater degraded image; s2, constructing an unsupervised deep learning image reconstruction network based on a physical model; s3, constructing a reconstruction network consistency verification module according to an underwater image imaging physical model and the network output reconstruction degraded image; s4, according to an image equivariant theory, constructing a fidelity image network equivariant loss and an underwater imaging equivariant loss; s5, according to the image isomorphism, constructing a fidelity image to restore an image isomorphism verification module; s6, according to the noise distribution of the underwater degraded image, constructing noise loss by using an unbiased estimation method, and correcting the deviation of reconstruction loss; s7, according to a gray world hypothesis, compensating the information of the blue channel and the green channel with relatively small color weakness to the red channel, and constructing a color fidelity verification module; and S8, carrying out constraint training on the network according to the steps S3-S7 until loss convergence, and obtaining a fidelity image estimation module for repairing the degraded image. The invention further provides a storage medium and electronic equipment.

The invention provides an underwater target detection system and method for an AUV-carried synthetic aperture sonar, and belongs to the technical field of underwater acoustic detection. The method comprises the following steps: transmitting an ultra-wideband sound wave signal to an underwater target, and collecting an echo signal by using receiving array elements which are non-uniformly and sparsely arranged along a track direction; preprocessing the echoes and decomposing the echoes into a plurality of non-overlapping sub-band signals, obtaining an equivalent space response by calculating inter-channel phase differences under each sub-band, and further constructing a joint observation matrix; and high-resolution azimuth scattering intensity distribution is recovered in combination with compressed sensing, and motion compensation is performed by using platform motion data to realize underwater imaging. According to the invention, the dependence of the traditional synthetic aperture technology on long-distance uniform-speed motion is broken through, and high-resolution and high-precision underwater target detection and imaging can still be realized under the condition of high-speed navigation of an AUV (Autonomous Underwater Vehicle).

Disclosed are imaging systems and methods for dimension measurement independent of laser alignment. An example method includes: obtaining an image of the object captured underwater using an underwater imaging system comprising a camera and a laser source, wherein the image includes a representation of the object and a laser spot incident on the object; identifying, in the image, at least two points of interest on the object; determining an image-space distance between the at least two points as represented in the image; determining a location of the laser spot within the image; estimating a camera-to-object distance based on the location of the laser spot within the image and a calibrated spatial relationship between the camera and the laser source; and estimating the physical dimension of the object based on the image-space distance using the camera-to-object distance.

The invention provides an ocean engineering pile foundation scouring experiment maximum scouring depth estimation method based on an integrated underwater imager. According to the integrated underwater imager, a self-adaptive algorithm is matched with a multi-wavelength laser technology, the laser intensity, the wavelength and the scanning speed are adjusted in real time according to the transparency and the turbidity of a water body so as to adapt to different water body conditions, and scour depth data are efficiently and accurately obtained in a complex underwater environment through data fusion processing in combination with a sonic sensor. In the experiment implementation process, the integrated imager carries out data scanning at regular time, the obtained depth-of-impact elevation data is rapidly processed, and the maximum depth of impact and the change rate of the maximum depth of impact are judged in real time. And when the change rate is less than 5% continuously twice, the experiment is judged to reach a balance state, so that time and manpower are saved. Afterwards, the ARIMA model is utilized to analyze and predict the maximum depth data obtained through previous scanning, a more accurate maximum depth value is obtained, and the defects of the experiment are further made up. The scientificity and practicability of the ocean engineering pile foundation scouring experiment are remarkably enhanced, and the ocean engineering pile foundation scouring experiment device has wide application prospects.

The invention discloses a self-adaptive range gating ocean laser radar, and belongs to the technical field of underwater laser imaging. According to the system, on the basis of a traditional range selection underwater laser imaging radar, the functions of receiving channel light path automatic switching and active distance measurement are added. Target distance information acquired by the distance measuring channel is actively fed back to the imaging camera, the imaging camera is accurately controlled to work only within the time when target echoes return, meanwhile, the receiving light path has an automatic switching function, a light path beam splitting mode is replaced, all laser energy is used for distance measuring or imaging, and the detection effect is guaranteed. The active distance measurement function is added, so that the system can adaptively control the gating time, the defect that the imaging radar needs to be continuously sliced under the traditional distance gating water is overcome, and the measurement speed of the system is greatly improved.

The invention discloses a two-dimensional polarization distance underwater imaging method under bionic vision inspiration, and relates to the technical field of underwater imaging, and the method comprises the following steps: S1, obtaining a polarization image; s2, extracting a low-frequency part; s3, calculating back scattering light AOP; s4, calculating backscattered light; s5, calculating the polarization sensitivity of the background; s6, calculating the polarization sensitivity of the target; s7, calculating the activity value of the background interneurons; s8, calculating the activity value of the target interneurons; and S9, obtaining a final image. According to the two-dimensional polarization distance underwater imaging method inspired by bionic vision, background interference can be effectively suppressed, and tiny polarization difference between a target and a background can be captured, so that target information can be clearly extracted in a complex water area environment, and compared with a traditional polarization imaging method, the method has the advantage that the imaging efficiency is improved. The method has obvious advantages in the aspects of image details, definition, stability and the like, and shows higher adaptability and reliability.

The invention discloses a three-dimensional Gaussian splash reconstruction method for an underwater scene, and belongs to the technical field of computer vision and three-dimensional reconstruction. Comprising the following steps: acquiring a monocular video frame sequence of a target underwater scene, a corresponding camera pose sequence, an initial sparse point cloud, an initial three-dimensional Gaussian point set and learnable physical parameters of an underwater imaging model; in the training process, performing weighted evaluation on a reconstruction error based on a multi-view consistency mechanism of opacity weighting, calculating an importance score of each Gaussian point, and performing densification operation on a three-dimensional Gaussian point set; adopting a staged freezing strategy to cooperatively optimize the three-dimensional Gaussian point set and underwater imaging model parameters; and performing rendering and underwater image synthesis on any new view angle camera pose based on the optimized three-dimensional Gaussian point set and underwater imaging model parameters, and outputting a new view angle synthesized image to represent a reconstruction result. According to the method, the geometric compactness, the visual fidelity and the physical interpretability of an underwater three-dimensional reconstruction result are improved.

The invention provides a polarization integrated imaging detection module applicable to a severe environment, and relates to the technical field of image detection. The system comprises an optical lens, a polarization CMOS sensor, a data transmission link, a control transmission link and an on-chip integrated processing system. Wherein the exposure consistency of the multi-polarization-angle images is ensured by performing acquisition and exposure adjustment on the images at four polarization angles and executing a brightness normalization algorithm, so that the problem of brightness unbalance during multi-image fusion of a traditional exposure algorithm is avoided; besides, through local dynamic range optimization, global gamma correction and a multi-scale fusion method, texture and brightness characteristics at different polarization angles are reserved, and clear and reasonably exposed image generation is realized. The defects in the aspects of atmospheric haze, underwater imaging and strong reflected light processing in the prior art are overcome, high-resolution and high-frame-rate real-time clear imaging is achieved, and reliable image support is provided for various application scenes.

The invention discloses a spectrum processing and deep learning combined underwater orbital angular momentum pattern recognition method, which comprises the following steps of: firstly, processing a signal by utilizing half-spectrum search and energy spectrum optimization, and constructing two-dimensional OAM (Orbital Angular Momentum) multi-channel data; then, sending the OAM multichannel data into a deep learning model to extract OAM features; and finally, sending the OAM features into a pre-trained classifier, carrying out matching and classification on the features, and outputting corresponding OAM mode categories. Based on a deep learning and spectrum processing collaborative optimization scheme, the precision and robustness of OAM mode recognition can be remarkably improved, technical support is provided for underwater high-capacity communication and environmental perception, and the method is suitable for multiple fields of underwater high-capacity communication, underwater target detection and recognition, marine environment monitoring, underwater imaging and the like. The method has important theoretical significance and wide practical application prospects.

The invention discloses an underwater polarization imaging method based on a deep learning algorithm, and the method employs a data collection module, a deep learning module and an image output module, and comprises the steps: collecting a large amount of polarization image data in different underwater scenes through the data collection module; inputting the data into a deep learning module to train a deep learning model to obtain a trained deep learning model; and inputting a newly collected underwater polarization image into the trained deep learning model to obtain a clear underwater imaging picture. Through mass data training, the model generalization ability is high, a good imaging effect on a new scene is achieved, meanwhile, parameters can be adjusted in real time, the imaging quality can be optimized, and high-quality image support is provided for underwater detection and other activities.

The invention discloses an underwater polarization imaging method based on a Stokes vector, and the method comprises the following steps: 1, obtaining a plurality of polarization images with different polarization directions, so as to obtain the Stokes vector of each polarization image; step 2, based on the Stokes vector of each polarization image, obtaining a Stokes vector model of back scattering light through frequency domain filtering; 3, according to the Stokes vector model of the backscattered light, obtaining a backscattered light image of any polarization angle, solving a maximum backscattered light image in combination with the low-rank characteristic of the backscattered light image, correlating a minimum backscattered light image by using the degree of polarization, and finally fusing the minimum backscattered light image into an underwater imaging physical model to establish a restored image inversion model; and 4, according to the restored image inversion model, obtaining a final imaging result.

The invention relates to the technical field of underwater detection, discloses a remote synchronous synthetic aperture sonar cluster acoustic compatibility method, and solves the problem of mutual interference caused when multiple synthetic aperture sonars of the same model carry out detection in the same area. The method comprises the following steps: acquiring the state position and course information of a cluster sonar unit; predicting a future track and a sonar underwater irradiation area based on extended Kalman filtering; an orthogonal frequency modulation emission signal is dynamically generated; on the basis of receiving mixed echoes, carrying out iterative interference cancellation by using known waveform information; and a sparse reconstruction theory is combined to recover a full-bandwidth target echo, and the full-bandwidth target echo is finally used for generating a high-resolution interference-free sonar image. The system comprises a state acquisition module, a track prediction module, a waveform generation module, a transmit-receive control module, an interference cancellation module, a frequency spectrum reconstruction module and an imaging module, and collaborative optimization of sonar interference suppression and underwater high-resolution imaging is realized.

The invention provides a self-supervised multi-task underwater image restoration method based on an underwater imaging model, and the method comprises the following steps: carrying out the color cast correction of an underwater image collected by an underwater robot through an adaptive color balance algorithm, and obtaining an image after the color cast correction; obtaining the transmissivity of the image after the color cast correction through a transmissivity estimation network; enabling the image after color cast correction to pass through a background light estimation network to obtain background light; enabling the image after color cast correction to pass through a clear image reconstruction network to obtain a reconstructed clear image; and the background light and the transmissivity of the reconstructed clear image are synthesized through a Jaffe-McGammery underwater imaging model, and training is carried out in a self-supervision mode to obtain a final enhanced image. According to the method, an end-to-end self-supervised learning architecture is adopted, network training can be completed without large-scale annotation of a data set, the method has the advantages of low training cost and high generalization ability, and efficient image restoration can be realized in a complex and changeable underwater environment.

The invention discloses an underwater high-repetition-frequency pulse laser line scanning imaging system and method, and relates to the technical field of underwater laser imaging, a laser emission module is used for emitting high-repetition-frequency pulse laser and comprises a high-repetition-frequency pulse laser device with the working wavelength of 532 nm and a beam shaping module; wherein the light beam shaping module is composed of a double-axis cone mirror and a cylindrical mirror; the laser imaging module is used for carrying out large-depth-of-field imaging on target reflection echoes in a set time window, comprises a high-speed gating camera, an imaging lens and a narrow-band optical filter, and is constructed based on the Sammer imaging principle; and the synchronous control module is used for performing power supply communication and gating time sequence control to realize time sequence synchronization of the laser and the camera in the system. According to the invention, laser line scanning and range gating imaging technologies are combined, so that the method is suitable for long-distance and large-range environment detection, high-precision three-dimensional reconstruction of a short-distance target can be realized, and the definition, the signal-to-noise ratio and the applicability of underwater imaging are effectively improved.

The invention provides an intelligent optical sensing system and method for imaging through an underwater turbid environment, and belongs to the field of underwater optical imaging. The problem of low underwater object recognition efficiency caused by difficulty in obtaining clear images in turbid water by existing equipment is solved; the system comprises an optical imaging module, an image acquisition module and computing equipment which are carried on diving equipment, computer programs of an image preprocessing module, a depth sensing module and a neural network denoising recognition module run on the computing equipment, and the optical imaging module generates a structured light field carrying an orbital angular momentum mode based on a spiral phase plate; the image acquisition module is used for acquiring reflection information of the target object irradiated by the vortex light; the image preprocessing module is used for enhancing the acquired image; the depth sensing module obtains the current water pressure through a pressure sensor, and the current water depth is inversely solved through the computing device; the neural network denoising recognition module carries out real-time recognition and denoising on the image; the method is applied to turbid underwater imaging.

The invention discloses an underwater image enhancement method based on a convolutional network constrained by an underwater imaging physical model, and aims to solve the problems of serious color cast and insufficient contrast, saturation and color richness of collected underwater images when an underwater robot explores an underwater world. The invention provides an underwater image enhancement method based on a convolutional network constrained by an underwater imaging physical model, and the method comprises the steps: carrying out the estimation of background light and a depth image through employing a convolutional neural network on the basis of an underwater imaging model, calculating a texture image of an image on the basis of a blurred image, and carrying out the calculation of the texture image of the image according to the texture images of different degrees of the image, and the image is enhanced from the angle of multi-scale optimization. Compared with the existing UIE algorithm, the method has the advantages that the problems of color deviation and contrast distortion are effectively eliminated while complex image details are kept, and the full-reference estimation and non-reference measurement indexes are obviously improved.

The invention relates to the technical field of optical imaging, and particularly provides an underwater ultra-wide-angle imaging optical lens and equipment, the underwater ultra-wide-angle imaging optical lens and equipment are suitable for a liquid medium with the refractive index of 1.3-1.4 and are especially designed for an underwater high-pressure environment, the lens sequentially comprises protective glass, a front lens group with positive focal power and a rear lens group with positive focal power in the light incidence direction, wherein the front lens group is composed of six lenses including a single lens and a glued lens, the rear lens group is composed of four lenses including a single lens and a glued lens, aberration correction is achieved by optimizing focal power distribution and glued structures of all the lenses, and the underwater field angle reaches 140 degrees or above within the waveband range of 450-700 nm. The system solves the problems of limited view field, low contrast ratio and the like caused by total reflection in underwater imaging, has the characteristics of miniaturization, wide view field and high resolution, and is suitable for scenes requiring deep underwater monitoring and monitoring. In addition, the optical system is a full-spherical-surface optical system and does not comprise an aspheric surface, and the economic benefits are effectively improved.

This application provides an intelligent optical sensing system and method for imaging in turbid underwater environments, belonging to the field of underwater optical imaging. It solves the problem of low efficiency in identifying underwater objects due to the difficulty of obtaining clear images in turbid water with existing equipment. The system includes an optical imaging module, an image acquisition module, and a computing device mounted on a diving device. The computing device runs computer programs for an image preprocessing module, a depth sensing module, and a neural network denoising and recognition module. The optical imaging module generates a structured light field carrying an orbital angular momentum mode based on a spiral phase plate. The image acquisition module acquires the reflection information of the target object after being illuminated by vortex light. The image preprocessing module enhances the acquired image. The depth sensing module obtains the current water pressure using a pressure sensor, and the computing device decomposes the current water depth. The neural network denoising and recognition module performs real-time image recognition and denoising. This application is applied to underwater imaging in turbid water.

The invention relates to the technical field of underwater imaging, provides a Fourier lamination underwater imaging system and method based on adaptive phase retrieval, and effectively solves the problem of insufficient imaging resolution caused by a strong scattering effect of a water body and aperture limitation of an optical system in a traditional underwater imaging technology. According to the method, the Fourier lamination imaging technology is applied to the underwater environment for the first time, and the spatial resolution is improved by 2.24 times compared with that of a traditional method in combination with the self-adaptive phase retrieval algorithm. Further experiments show that even under the strong scattering condition, the scheme can still keep the resolution performance obviously superior to that of a traditional method. The invention provides a novel underwater high-resolution imaging technical scheme, the imaging performance of marine resource exploration and underwater engineering facility detection can be remarkably improved, and the method has important application value.

The invention relates to the technical field of underwater imaging equipment, and discloses an underwater variable baseline stereoscopic vision imaging system, which comprises a mounting plate, a lifting mechanism is arranged at the lower part of the mounting plate, the lifting mechanism is used for driving a direction adjusting cylinder to lift, a direction adjusting mechanism is arranged in the direction adjusting cylinder, and the direction adjusting mechanism is used for adjusting the direction of the direction adjusting cylinder. The direction adjusting mechanism is used for adjusting the direction of the groove frame, the groove frame is provided with an angle adjusting mechanism, the angle adjusting mechanism is used for adjusting the angle of the shell, continuous adjustment of the baseline length of the left camera and the baseline length of the right camera can be synchronously achieved, and the visual angle included angle between the two cameras is adjusted in a linkage mode. It is ensured that the imaging system always maintains a stable imaging geometrical relationship and reasonable field-of-view overlapping; the design has the advantages of being compact in structure, easy and convenient to control, sensitive in response and the like, is particularly suitable for underwater stereoscopic vision imaging tasks in a dynamic change environment, and has good engineering application prospects.

This invention provides an underwater long-range ultra-high-definition polarization imaging system and method, relating to the field of data processing technology. The system comprises, from bottom to top, an optical sensing layer, a physical computing layer, an AI inference enhancement layer, an adaptive control layer, and a decision optimization layer, forming a hierarchical control structure where data flows upward and strategies are passed downward. The optical sensing layer employs a non-uniform polarization sampling array, a spectral polarization joint coding unit, and a dual-modal illumination unit to simultaneously acquire intensity, multi-directional polarization, multispectral images, and depth information. The physical computing layer calculates polarization parameters in real time and inverts water turbidity within the image signal processor hardware pipeline. The AI ​​inference enhancement layer embeds the physical model as a differentiable module into a neural network to achieve scattering suppression and detail enhancement. The adaptive control layer adopts a time-scale separated dual-closed-loop architecture. This invention can adapt to different water turbidity levels, output ultra-high-definition images in real time, and significantly improve the quality of long-range underwater imaging.

The invention discloses an underwater binocular camera calibration method based on laser assistance, and the method comprises the steps: firstly constructing an underwater stereoscopic vision binocular refraction-removing imaging model according to the Snell's law, and correcting an underwater imaging error; secondly, acquiring a camera internal reference matrix by using a Zhang Zhengyou calibration method; then, a laser range finder is used for projecting laser to the calibration plate to form a light spot, the center pixel coordinate of the light spot is extracted through a gray gravity center method, the three-dimensional coordinate of the light spot is calculated in combination with laser ranging data, and the initial value of an external parameter matrix of the binocular camera is solved; and finally, introducing a Bayesian optimization algorithm, and optimizing the constraint equation by taking minimization of a three-dimensional coordinate calculation error as a target so as to obtain an exact solution of an external parameter matrix of the camera. Through organic combination of a deep learning technology and binocular stereo vision, the parameter calibration precision of the underwater binocular stereo vision camera is improved, a reliable technical guarantee is provided for subsequent underwater concrete structure defect quantification, and the fine monitoring requirement in a complex water environment is met.

This invention discloses an underwater image enhancement method and system based on physical priors and target features, belonging to the field of image processing technology. To address the technical problem that existing underwater image enhancement methods struggle to simultaneously achieve imaging physical consistency, visual quality, and usability for downstream target recognition tasks, this invention extracts physical prior information from underwater images, including transmittance maps and background illumination maps; acquires target features from underwater images; extracts image features from underwater images through an enhancement network; and fuses and reconstructs image features, physical prior information, and target features to obtain an enhanced image; trains the enhancement network; and uses the trained enhancement network to process underwater images to obtain the enhanced image. This invention achieves high-quality visual restoration while satisfying the physical constraints of underwater imaging, and significantly improves the accuracy of underwater target recognition and the performance of related downstream tasks.

The present invention discloses an OSIC detection method for an underwater optical Massive MIMO communication system with misaligned transmission and reception. After estimating the gain of each sub-channel using the LS channel estimation algorithm, the offset direction of the transceiver can be determined based on the magnitude of each DC gain in the channel gain matrix. The detection column order and the order within the column are then determined based on the offset direction, and interference elimination is performed to complete the detection of each signal. The present invention sorts according to the offset direction, preferentially detecting signals without interference, and then detecting signals with interference, greatly reducing the error propagation caused by the sorting, making the subsequent stage more accurate in subtracting the detected signal, and making the remaining signal used in the subsequent stage have less interference, thereby improving the bit error rate performance, so as to solve the problem of increased bit error rate of the UWOC system caused by misalignment of the transceiver and offset and diffusion of the imaging spot in the underwater imaging optical Massive MIMO communication system.