63 results about "Underwater image processing" 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 image semantic segmentation method and device based on a lightweight double-flow Mama network, and a storage medium, relates to the technical field of underwater image processing, and solves the problems of poor environmental adaptability, low modal fusion efficiency, heavy model and high calculation overhead in the existing underwater image semantic segmentation technology. According to the method, a double-branch encoder is constructed, the double-branch encoder composed of an image encoder and a text encoder is adopted, and a visual feature map and a semantic feature vector in a preprocessed underwater image and text description information are extracted respectively; a cross-modal Mama module is adopted to carry out deep fusion on a flattened image feature sequence and text features in the module, the cross-modal Mama module adopts a Mama block with linear complexity, and continuous guidance and progressive enhancement of text semantics are realized in combination with a multi-level gating fusion mechanism and residual connection. And the recognition capability of underwater fuzzy and shielded targets is remarkably improved, and meanwhile, the calculation efficiency is remarkably improved.

The invention discloses a lightweight underwater image enhancement method, device and equipment based on a semi-supervised inactive function, and a storage medium, and relates to the technical field of underwater image processing. The lightweight underwater image enhancement method based on the semi-supervised inactive function comprises the following steps: constructing an initial image enhancement model based on a teacher-student framework of semi-supervised comparative learning and a gradient inactive function network; training the initial image enhancement model through label data and label-free data to obtain a target image enhancement model; obtaining an underwater image to be enhanced; and inputting the underwater image into the target image enhancement model to obtain an enhanced image. According to the method, the image can be efficiently processed, the cross-scene adaptive capacity of underwater image enhancement can be improved, the model deployment cost is reduced, and powerful technical support is provided for applications such as smart fishery.

The invention discloses an underwater target volume measurement method, device and equipment based on three-dimensional reconstruction and a medium, and belongs to the technical field of photogrammetry, underwater image processing and point cloud volume calculation cross, and the method comprises the steps: carrying out the video data collection of an underwater target through video collection equipment, and obtaining an original image with image format data; the underwater image enhancement method model based on depth estimation performs image enhancement on the original image to obtain an underwater enhanced image; reconstructing a real scene containing a target based on the underwater enhanced image; and based on the three-dimensional reconstruction point cloud, segmenting to obtain the to-be-measured target, and obtaining the volume parameter of the underwater target. According to the method, a three-dimensional reconstruction technology is utilized, a real target structure is restored, the measurement accuracy of the underwater target defect volume is improved, a reference standard is provided for later repair, the maintenance cost is reduced, and the operation safety of a hydraulic structure is guaranteed.

The invention relates to the technical field of computer vision, in particular to a marine organism intelligent detection system based on multi-scale convolution fusion and YOLOv8, and the system comprises an acquisition module which is used for obtaining the original image data of underwater marine organisms, and processing the original image data to obtain a marine organism data set; the detection module is used for identifying the marine organism data set; through the VM-Unet improved multi-scale underwater image enhancement network and the deeply optimized YOLOv8 algorithm, lightweight and efficient calculation, multi-scale feature fusion and complex scene robust recognition are realized, the precision and real-time performance of underwater image processing and target detection are remarkably improved, and the method has the advantages of being high in robustness and high in robustness. And a technical support with high adaptability and high efficiency is provided for a marine organism intelligent detection system.

The invention discloses an underwater hyperspectral image clustering method based on a multi-scale anchor image, and belongs to the technical field of underwater image processing. The method mainly comprises the following steps: carrying out superpixel segmentation on an underwater hyperspectral image; performing noise removal processing on the segmented hyperspectral image; learning a multi-scale anchor image of the denoised hyperspectral image; matrix decomposition is carried out on the multi-scale anchor images, and soft labels of super-pixel points are obtained; stacking the soft label matrixes into a tensor, and introducing a tensor Schatten-p norm to obtain a high-dimensional space structure of the multi-scale anchor map; constructing an optimization objective function; updating the target function variable by using an iterative updating strategy; and carrying out adaptive fusion on the learned soft label matrix. According to the method, pixel point soft labels are obtained mainly through multi-scale anchor image decomposition, label matrixes are stacked into tensors to explore a high-dimensional space structure of data, finally, self-adaptive weighted summation is conducted on the soft labels of the multi-scale anchor images to obtain a label result used for clustering analysis, and the clustering precision is remarkably improved.

The invention discloses an underwater polarization image restoration method based on Transform and depth estimation, and belongs to the technical field of underwater image processing. The method comprises the following steps: acquiring an underwater polarization image data set, and dividing the underwater polarization image data set into a training set and a test set; training an underwater polarization image restoration network based on Transform and depth estimation, the network comprising an encoder and a decoder, the encoder comprising a U-Net module, a multi-scale content guidance attention module, a depth estimation network and a multi-scale convergence attention module; the decoder comprises two feature fusion modules which are connected in series and guide attention based on multi-scale content; the trained underwater polarization image restoration network based on Transform and depth estimation is tested based on the test set; and carrying out image restoration by using the tested underwater polarization image restoration network based on Transform and depth estimation. According to the method, the restoration effect of the underwater image can be remarkably improved, the image texture information is enhanced, and the definition and detail performance of the image are improved.

This invention provides a semantically driven, frequency-consistent underwater image enhancement method, belonging to the field of underwater image processing technology. The specific steps include: acquiring both degraded and clear underwater images of the same scene; extracting the low-frequency components of both; calculating and obtaining the global low-frequency residual components; generating a semantic partition mask through semantic segmentation; dividing regions by combining gray-level variance to obtain the final degraded region; introducing semantic adaptive coefficients to output semantic enhancement features; combining the semantic adaptive coefficients and gray-level variance to obtain fusion weight coefficients; weightedly fusing the semantic enhancement features with the local low-frequency residual components to obtain fusion features; inputting the combined feature map into an image enhancement network model; outputting clear underwater image fragments; and stitching them with the original image of the non-degraded region to obtain a complete underwater enhanced image. This invention achieves accurate quantification and frequency domain compensation of global degradation differences, solves the problem of global and local enhancement imbalance, and improves the integrity and consistency of underwater image enhancement.

The invention relates to the technical field of underwater image processing, in particular to a multi-layer tensor-based incomplete underwater hyperspectral image clustering method, which comprises the following steps of: acquiring an image to be processed, and acquiring incomplete spectral data of the image to be processed in different intervals; a clustering model based on spectral clustering learning is constructed, incomplete spectral data is processed based on the clustering model, and the clustering model comprises a projection operator information extraction module, a multilayer graph learning module, an adaptive weight module, a multistage tensor learning module and a nuclear norm constraint module; and iterating the hyperspectral clustering model based on an alternative update variable strategy until an iteration stop condition is met, and outputting a clustering result based on spectral clustering. According to the method, unified similar images are dynamically learned in all pixel points, the problems of view missing and incomplete observation can be effectively solved, complementarity among multiple views and hierarchical structure characteristics of underwater targets can be fully mined, and the method is suitable for unsupervised clustering tasks of complex underwater hyperspectrums.

The application 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.The application combines deep learning pose estimation with an underwater imaging model; firstly, a plurality of underwater image sequences are collected as input to construct a deep neural network, the network mainly comprises two core sub-modules: one is a pose estimation network; and the other is a three-dimensional reconstruction network, according to the predicted pose and image content, dense point cloud or voxel reconstruction is completed, the pose estimation and the three-dimensional reconstruction process are integrated in the same system to realize overall joint optimization; and the adaptive underwater imaging model is combined to model the physical processes such as underwater light attenuation and scattering, so that the realism and accuracy of the reconstruction result are improved.The application realizes high-quality three-dimensional reconstruction of images in a complex underwater environment, and can be widely applied to ocean engineering, underwater robots, seabed topographic mapping and underwater cultural relic protection.

The invention discloses a non-pairing underwater image enhancement method based on structure perception multi-mode fusion, and belongs to the technical field of underwater image processing. Comprising the following steps: constructing a generator network based on a U-shaped Transform; training a generator network by using a non-paired data set, and carrying out combined constraint by using generative adversarial loss and multi-scale Patch NCE comparison loss; and performing channel splicing on a to-be-processed underwater RGB image, the space depth image and the structure prior image, constructing a multi-modal input tensor, then feeding the multi-modal input tensor into the trained generator network, and outputting an enhanced underwater image through forward propagation. According to the method, the depth map and the structure prior map are introduced, the perceptual ability of the network to the geometric contour is enhanced, global modeling and non-pairing comparative learning of Transform are combined, high-fidelity restoration of the high-turbidity underwater image is achieved under the condition that paired data is not needed, and the method can be widely applied to the fields of ocean exploration, underwater monitoring and the like.

The application discloses a dark underwater image processing method based on color space conversion, which comprises the following steps: firstly, pre-acquired underwater images are down-sampled to reduce the image size and copied into two images; secondly, color space conversion is performed on the two parts of underwater images respectively; thirdly, an underwater optical imaging image model is established, and the first converted underwater image is processed by using an image deblurring algorithm to obtain a clear image with high contrast; fourthly, an improved white balance algorithm is used to process the deblurred image; fifthly, a nonlinear channel prior is introduced into a traditional image deblurring model to minimize the item to eliminate the motion blur problem caused by water flow fluctuation; sixthly, the obtained accurate blur kernel and potential clear image are iterated with the initial restored image to obtain a final restored image; and finally, the two images are fused, re-sampled and outputted to obtain a final clear underwater image. The application can better realize image restoration, improve the brightness and contrast of the image and improve the recognition degree.

The invention provides an underwater target detection method based on gating guidance and feature supplementation, relates to the technical field of underwater image processing, and particularly mainly adopts the idea of gating guidance feature fusion and multi-scale feature supplementation to dynamically select and weight features of different levels through a gating mechanism, so that the detection accuracy of the underwater target is improved. Redundant features are effectively filtered, significance of target information is enhanced, and expression of target related features is enhanced. And aiming at the problem that the feature information of the underwater small target is fuzzy along with the increase of the depth of the network, the method adopts the idea of multi-scale feature supplementation, improves the receptive field and semantic expression of shallow features, and improves the detection performance of the small target. And finally, training and reasoning are carried out based on the real scene underwater data set, and accurate underwater target positioning information is obtained.

The invention provides an underwater image enhancement method based on space and scale context awareness, which relates to the technical field of image processing, and comprises the following steps of: under a generative adversarial network framework, introducing a context awareness strategy, and combining a space context feature enhancement module and a scale context interaction module to obtain an underwater image enhancement result; and the detail recovery, color correction and noise suppression capabilities of the underwater image are effectively improved. According to the method, the image can be optimized on different scales, the noise problem in underwater image degradation is solved, meanwhile, the sense of reality and the visual quality of the image are enhanced, and a new solution is provided for the field of underwater image processing.

The invention relates to the technical field of underwater image processing, in particular to an underwater image enhancement method based on double-branch complementary input and cross-branch cross-layer state transition, and the method comprises the steps: obtaining an underwater image; an underwater image enhancement network model is constructed, the underwater image enhancement network model comprises a first branch, a second branch and a state transition module, the first branch comprises a self-adaptive feature extraction module, a multi-scale spatial pyramid pooling module, a channel splicing fusion layer and a tail end self-adaptive feature extraction module, and the second branch comprises a multi-scale spatial pyramid pooling module. The second branch comprises a channel intensity inversion layer, a self-adaptive feature extraction module, a multi-scale spatial pyramid pooling module, a channel splicing fusion layer and a tail end self-adaptive feature extraction module; training an underwater image enhancement network model; and inputting the test set into the trained model, and outputting an enhanced underwater image. According to the method, the color recovery precision and the detail retention performance of the underwater image in a variable environment are improved.

This invention discloses an underwater target saliency detection method and system based on polarization multi-stage fusion, belonging to the field of computer vision and underwater image processing technology. The method acquires four-angle polarization images of turbid water and calculates the degree of linear polarization, then normalizes them to generate a polarization-enhanced image. Subsequently, high-frequency edge components are extracted through brightness enhancement and the Laplacian operator, and a preprocessed image is obtained by injecting learnable weights. A pre-trained deep neural network is then used to fuse the original image and the preprocessed image, outputting a saliency prediction map. This invention fully exploits polarization features, effectively suppresses scattering noise, and significantly improves the precision of underwater target edge segmentation.

The invention relates to an underwater image processing method based on multi-dimensional weighting, and belongs to the technical field of data processing, and the method comprises the steps: carrying out the zooming processing of an underwater target image, and obtaining a feature image after the zooming processing; adding a multi-dimensional feature enhancement module MFEM into the neck network of the neural network model to obtain an image processing model; performing feature extraction on the scaled feature image to obtain a feature image after feature extraction; dimensionality adjustment and average pooling are carried out on the feature image after feature extraction, attention weights are generated in the channel dimensionality, the height dimensionality and the width dimensionality respectively, the attention weights and the feature image after feature extraction are multiplied element by element, and then a feature image after feature fusion is obtained through connection. The problem of insufficient sensitivity of target edge and position information is solved, key target features can be self-adaptively enhanced, background interference is suppressed, and the image processing capability in a complex underwater environment is remarkably improved.

This application relates to the fields of underwater image processing and compressed sensing technology, and in particular to a multi-sampling-rate joint underwater image compressed sensing network. It includes: a dual-branch dynamic sampling network that generates an adaptive sampling matrix based on the target sampling rate, underwater image, and depth map to achieve accurate capture of key information; a structure-guided dynamic reconstruction network that receives sampled data, depth map, and target sampling rate, and dynamically adjusts the reconstruction strategy using a structure-adaptive reconstruction module to complete image reconstruction; and a hierarchical loss function used to train the network, dynamically adjusting the weight coefficients of each loss term according to the target sampling rate to balance reconstruction requirements at different sampling rates. Through a unified architecture that adapts a single model to multiple sampling rates, it significantly reduces model training and deployment costs while comprehensively improving the quality and stability of underwater image reconstruction from extremely low to high sampling rates.

The application discloses a background light estimation method in the technical field of image processing, an underwater image restoration method and electronic equipment, and aims to solve the problem of poor accuracy caused by unified estimation of the background light of three channels of RGB in the prior art. The application determines the decay rates of three color channels through the total pixel value size of the three color channels of the original input image, screens the pixels of the three color channels according to the pixel values of the neighborhood pixels, calculates the background light estimation values of the three color channels through different methods respectively, and then restores the three channels respectively. The application can be used for underwater image processing, can realize one-to-one accurate estimation of the background light of three channels of RGB, and improves the underwater image restoration quality.

The application belongs to the field of image processing, and discloses a kind of underwater image processing technology based on light diffusion model, aiming at solving the serious degradation problem of underwater image, conducive to the development of subsequent tasks. First, analyze the color distribution of the image in the reference underwater data set as priori;Second, define the search space and neural network of the neural architecture search algorithm;Third, use the distribution priori to guide the training process of the latent diffusion model;Finally, according to the combination of multiple network modules obtained by the neural architecture search algorithm, combine multiple latent diffusion models and evaluate the model performance, and select the model for different terminal devices according to the performance. The application can enhance the quality of underwater images, and use the data distribution learning advantage of diffusion model to generate underwater images with stronger diversity. In addition, the application will also automatically search for suitable diffusion models according to the resource limitations of terminal devices, achieving the purpose of lightweight design.

The application relates to the technical field of image enhancement, and discloses an underwater image enhancement method based on an improved ARLTGAN model, which comprises the following steps: obtaining an original image shot by an underwater robot, performing normalization preprocessing, and forming a to-be-processed image; loading an underwater image enhancement model, wherein the underwater image enhancement model is generated by training an improved ARLTGAN model and is used for realizing low-quality degraded underwater image I LR denoising, pixel enhancement, and outputting a high-resolution image I output ; the underwater image enhancement model comprises a generator and a discriminator; the to-be-processed image is input into the underwater image enhancement model, and a high-resolution image I output is output. The improved ARLTGAN model is introduced, the network architecture of the generator and the discriminator is innovated, a multi-source loss function is set, high-quality enhancement of the underwater image and highlighting of key defect features are realized, and the technical defects that the existing underwater image processing technology is not suitable for complex underwater environments are overcome.

This invention discloses a method and system for continuous frame target detection in sonar images based on tracker fusion, belonging to the field of underwater image processing technology. The method includes: acquiring target detection results from a sonar image at a given time, wherein the target detection results consist of at least one detection point; predicting the trajectory points of each target at a given time based on the tracker's stored time-based tracking trajectories; and generating the target detection results at that time based on the association cost matrix between all detection points and all trajectory points. This invention can effectively handle both dynamic and static targets, improving its practicality for real-world applications.

The invention relates to the technical field of image enhancement, and discloses an underwater image enhancement method based on an improved ARLTGAN model, and the method comprises the steps: obtaining an original image shot by an underwater robot, carrying out the normalization preprocessing, and forming a to-be-processed image; an underwater image enhancement model is loaded, and the underwater image enhancement model is generated by training of an improved ARLTGAN model and used for achieving ILR denoising and pixel enhancement of the low-quality degraded underwater image and outputting a high-resolution image Ioutput; the underwater image enhancement model comprises a generator and a discriminator; and inputting the image to be processed into the underwater image enhancement model, and outputting a high-resolution image Ioutput. According to the method, the improved ARLTGAN model is introduced, the network architecture of the generator and the discriminator is innovated, a multi-source loss function is set, underwater image high-quality enhancement is realized, key defect features are prominent, and the technical defect that an existing underwater image processing technology is poor in adaptability to a complex underwater environment is overcome.

The application discloses an underwater image enhancement method based on multiscale feature extraction and transmittance map guidance, and belongs to the technical field of underwater image processing. The method comprises the following steps: acquiring an underwater image training dataset; training an underwater image enhancement model based on multiscale feature extraction and transmittance map guidance based on the underwater image training dataset; acquiring underwater image data to be processed; inputting the underwater image data to be processed into the trained underwater image enhancement model based on multiscale feature extraction and transmittance map guidance; and acquiring an enhanced image output by a decoder. The application effectively captures color distortion areas by using frequency domain information, and accurately captures fogging distribution of a picture by using spatial domain information, thereby solving the problems of color distortion and fogging characteristics of high-frequency signals, and improving the enhancement effect of the image. In addition, the spatial and temporal information of a fused image is enhanced through a transmittance map guided enhancement process, and the physical interpretability and generalization ability of the model are further improved.

The application provides a kind of underwater target detection method and system based on multi-beam forward-looking sonar image, belongs to underwater image processing technical field.The method includes using multi-beam forward-looking sonar device to obtain target sonar image, using improved detection model based on convolutional neural network to extract multi-scale feature map, and to carry out boundary box prediction and classification, output contains the position coordinates of each detection target, class probability result information.In the improved convolutional neural network in the application, dynamic perception patch attention mechanism is introduced, combined with multi-branch feature extraction strategy, and a special small target detection head, enhance the detection ability to small target, to improve the detection precision purpose.In addition, the application integrates sonar imaging and processing, data communication and display control, etc.on embedded platform, realizes portable real-time detection, data exchange and user control.The application can effectively improve the detection precision of small target in underwater forward-looking multi-beam sonar image.

The invention discloses an underwater image sharpening method based on a feedback network and reinforcement learning optimization, and belongs to the technical field of underwater image processing. The method comprises the following steps: acquiring underwater images in various underwater environments and constructing a feedback data set; designing an underwater image sharpening network based on a feedback network and reinforcement learning optimization, wherein the network comprises an adaptive enhancement module, a scoring feedback network and a reinforcement learning optimization enhancement network; training a score feedback network by using the feedback data set; and based on a score feedback network and a reinforcement learning optimization mechanism, carrying out adaptive training on the underwater image sharpening network, and finally generating a high-quality underwater image. According to the method, automatic scoring is performed through a feedback network, an image sharpening strategy is dynamically optimized in combination with reinforcement learning, the color recovery, contrast enhancement and detail retention capabilities are improved, a clear and natural underwater image can still be generated in a high-turbidity and complex illumination environment, and the effectiveness and robustness of underwater visual perception are improved.

The invention discloses an underwater hyperspectral consensus clustering method based on orthogonal anchor points, and belongs to the technical field of underwater image processing. The method mainly comprises the following steps: forming an initial anchor point matrix and shared consensus representation by adopting a self-adaptive anchor point sampling strategy; linearly representing the anchor point matrix and the data matrix of each view by a clustering indication matrix and a view-specific orthogonal clustering center matrix; bidirectional space alignment is carried out on the consensus representation to reach a consensus; constructing an underwater hyperspectral consensus clustering model based on subspace learning, and updating a certain variable by using an iterative updating strategy under the condition that other variables are fixed until convergence; and obtaining a final clustering result according to the solved data clustering indication matrix. According to the method, a double-space clustering alignment joint optimization framework based on orthogonal anchor points is constructed. According to the framework, an anchor point space representation with discriminant force is generated by using orthogonal anchor points, and the anchor point space representation and a data space representation are mutually calibrated and enhanced, so that the clustering accuracy is remarkably improved.

The invention discloses an underwater image enhancement method and system based on three-channel stochastic resonance, and belongs to the technical field of underwater image processing. In order to solve the problems of low contrast, serious noise interference and the like of a turbid underwater image caused by strong backscattering, a stochastic resonance enhancement frame matched with the physical characteristics of an RGB channel is constructed. Target information is regarded as a weak signal, detail amplification is achieved through noise energy transfer, a multi-target parameter adaptive module for resolution perception is designed, and SR system parameters are dynamically adjusted. The method can effectively suppress noise, reserve texture details and improve the overall perception quality of underwater images, and is suitable for enhancement tasks of low-quality and strong-scattering underwater images.

The present invention relates to the field of underwater image processing technology, and in particular to a semi-supervised underwater image enhancement method based on multi-scale context perception, comprising: establishing an underwater image enhancement network model, the underwater image enhancement network model comprising a parallel image restoration branch and a detail restoration branch, the image restoration branch comprising a multi-scale input-level feature fusion module, a multi-branch hybrid convolution attention residual module, a downsampling module, and an upsampling module; the detail restoration branch comprising a pixel differential convolution layer, a normalization layer, and a channel-aware feedforward neural network; element-by-element addition of the outputs of the image restoration branch and the detail restoration branch to obtain the output of the underwater image enhancement network model; training the underwater image enhancement network model through a semi-supervised learning strategy and a joint loss function; and inputting an underwater image into the trained underwater image enhancement network model to obtain an enhanced underwater image. The present invention improves the enhancement effect and cross-scene generalization performance of underwater images.