129 results about "Image translation" patented technology

The Abstract of the Disclosure is provided to allow the reader to quickly ascertain the nature of the technical disclosure. It is submitted with the understanding that it will not be used to interpret or limit the scope or meaning of the claims. In addition, in the foregoing Detailed Description, it can be seen that various features are grouped together in various examples for the purpose of streamlining the disclosure. This method of disclosure is not to be interpreted as reflecting an intention that the claims require more features than are expressly recited in each claim. Rather, as the following claims reflect, inventive subject matter lies in less than all features of a single disclosed example. Thus, the following claims are hereby incorporated into the Detailed Description, with each claim standing on its own as a separately claimed subject matter.

The invention relates to an ultra-short-term photovoltaic power prediction method and device based on random skyline video prediction, and a medium. The method comprises the following steps: S1, extracting color cloud picture feature points from a foundation cloud picture sequence; s2, carrying out feature matching by adopting an FLANN algorithm, carrying out filtering correction by adopting an IRANSAC algorithm, and calculating a feature point coordinate transformation matrix according to a cloud picture feature point coordinate matching condition of adjacent time points to obtain a cloud cluster movement track; s3, if the cloud layer displacement speed is greater than a set threshold value, turning to S4, otherwise, performing image translation operation and then turning to S5; s4, adopting a SkyGPT model to carry out random skyline video prediction, and generating a skyline image sequence of a future set time period; s5, extracting the cloud cluster by adopting a threshold segmentation method, and constructing an irradiation coefficient representing the irradiance condition at the next moment; and S6, constructing an incidence matrix of the image and the irradiation coefficient, extracting irradiance, and fusing image features and irradiance features to obtain a photovoltaic power prediction result. Compared with the prior art, the method has the advantages of high prediction precision, high reliability and the like.

The invention provides an aircraft thin-wall component positioning rigidity characterization method based on a generative model, and relates to the field of aircraft equipment intelligent manufacturing, and the method comprises the steps: generating a process characteristic graph corresponding to a rigidity field through constructing a three-dimensional digital model and actual process parameters; acquiring a data set for model training; constructing a generative model based on an image translation mechanism and a conditional generative adversarial network; fine training is carried out on the model by inputting brand new data of'unseen 'of the network; the complete refined training model can adapt to various components, so that the time cost of rigidity field simulation is effectively reduced. According to the method, an effective solution is provided based on the process design of the stiffness field, digital twinning, augmented reality visualization and the like.

The invention discloses a migration method and device from simulation to a real image for an unmanned system, and the method comprises the steps: constructing a double-flow architecture comprising a convolution encoder and a visual Mama encoder, wherein the convolution encoder is used for extracting the local texture information of an image, and the visual Mama encoder employs a visual state space model to capture the global context information of the image; and the two features are input into a decoder after channel dimension fusion, and a real style image after style migration is generated. In order to improve the unsupervised learning ability of the model, a cyclic consistency training framework is introduced, training can be completed under the condition of no paired image samples, and the image structure consistency is kept. According to the method, the sense of reality and the structural fidelity of a simulation image are effectively improved, and compared with an existing method, the method shows lower distortion and higher generalization ability in an image translation task. The method can be widely applied to simulation data field adaptation and training sample enhancement tasks in the fields of automatic driving, virtual simulation, robot vision and the like.

The invention discloses a grayscale image translation motion blur recovery method and system based on optical flow guidance, and belongs to the technical field of image processing. A target area is determined based on a video stream H; calculating a translational motion vector field matrix between every two adjacent frames in the video stream H by adopting an optical flow method, obtaining a translational motion vector located in a target area, further constructing a track representation matrix for reflecting a motion track of a motion target generating translational motion, normalizing the track representation matrix to serve as a blurring kernel, and obtaining a motion vector field matrix; and translational motion blur of the target area is removed in a targeted manner. According to the method, the high-density time sampling information provided by the video stream H with the high frame rate and the low signal-to-noise ratio is utilized, and the complex translational motion track of the blurred image B with the low frame rate and the high signal-to-noise ratio in the exposure period can be accurately captured. The fuzzy kernel synthesized based on the accurate translational motion trails can highly approach a real physical fuzzy process, and an image with a high signal-to-noise ratio and high definition can be reconstructed.

The invention discloses a multi-modal fusion image translation method and system based on large language model prior, and the method comprises the steps: obtaining a registered infrared-visible light fusion image and a corresponding semantic mask and text description, and carrying out the data preprocessing, and obtaining a fusion image feature, semantic mask visual feature and text semantic feature sequence; constructing a multi-modal fusion image modal translation model based on the text-visual state space block and the three-dimensional selective scanning block; and performing image translation processing on the fused image features, the semantic mask visual features and the text semantic feature sequence based on a multi-modal fused image modal translation model to obtain a translated target image with visible light distribution characteristics. According to the method, the long-term dependency relationship can be captured through interaction among the text, the mask and the image, and the precision of multi-modal fusion image translation is improved. The multi-modal fusion image translation method and system based on large language model prior can be widely applied to the technical field of image processing.

The invention discloses a multi-modal remote sensing image matching method based on modal transformation and comparative learning. The method comprises the following steps: S1, constructing a training data set; s2, constructing a generation path of an image continuous domain; s3, designing a shortest path constraint; s4, defining the total loss of image conversion; s5, extracting rotation invariance feature expression of comparative learning; and S6, establishing a feature matching framework, and outputting a matching result. According to the method, potential sharing features are mined, the generation process of image translation is converted into a step-by-step generation mode based on a network path, the shortest path constraint is realized by constructing an intermediate domain, and the significant nonlinear radiation difference and noise between a reference image and an image to be registered are effectively eliminated; and meanwhile, by utilizing an EfficientNet architecture optimized by an expansion convolution and an attention mechanism, the representation quality of deep features is remarkably improved, and a training system of sample augmentation is designed in combination with twinning and pseudo-twinning networks to obtain better rotation consistency feature expression, so that the matching robustness of a descriptor to a weak texture region is effectively enhanced.

The invention relates to an aerospace target radar image data generation method based on low illumination enhancement correction. The method comprises the following steps: designing a content delivery decomposition network, decomposing low light characteristics into illumination-independent reflection components and adaptive illumination components through Hadamard product constraint in a submerged space, proposing a learnable intensity compression function, and dynamically generating a submerged space mask through gradient consistency constraint and sparse regularization; embedding a mask into a reverse denoising process to realize directional repair of a degraded region, and combining the generation capability of a diffusion model with the guidance of physical prior; a diffusion path is reconstructed based on a bidirectional diffusion principle, physical consistency of a generation process is constrained through end point binding, a diffusion step length is adaptively adjusted, a cyclic implicit iteration mechanism is introduced, a generation result is gradually refined through a cyclic neural network module, and accurate optical-ISAR image translation can be realized.

The present invention discloses a semantic segmentation method for cross-satellite remote sensing images based on unsupervised bidirectional domain adaptation and fusion. The method includes training of bidirectional source-target domain image translation models, selection of bidirectional generators in the image translation models, bidirectional translation of source-target domain images, training of source and target domain semantic segmentation models, and generation and fusion of source and target domain segmentation probabilities. According to the present invention, by utilizing source-target and target-source bidirectional domain adaptation, the source and target domain segmentation probabilities are fused, which improves the accuracy and robustness of a semantic segmentation model for the cross-satellite remote sensing images; and further, through the bidirectional semantic consistency loss and the selection of the parameters of the generators, the influence due to the instability problem of the generators in the bidirectional image translation models is avoided.

The invention provides a medical image registration method and system based on image-to-image translation, and belongs to the technical field of medical image registration, and the method comprises the steps: obtaining a linear X-ray image, processing the linear X-ray image, and inputting the processed linear X-ray image to an image-to-image translation module to obtain a simulation DRR image; inputting the obtained linear X-ray image into a pose initialization module to obtain an initialized pose; setting the obtained initialized pose as a global variable, inputting the global variable into an optimizer, down-sampling a simulation DRR image and a standard DRR image to a certain scale, calculating the image similarity between the images, inputting the image similarity into the optimizer, continuously updating the pose output by the optimizer, and carrying out optimization of different scales to obtain a final pose. And calculating a target registration error based on the final pose.

The invention provides an image enhancement method and device, electronic equipment and a storage medium, and the method comprises the steps: obtaining a to-be-processed source domain image and a to-be-processed noise image, extracting the edge information of the source domain image, inputting the edge information and the noise image into a preset image enhancement diffusion model, and obtaining an enhanced target domain image, according to the method, conversion from the source domain image to the target domain image is achieved, the target domain image is generated by guiding the image enhancement diffusion model through the edge information, details of the source domain image can be reserved, the fidelity and the structural similarity of the enhanced image are greatly improved, and compared with an existing image translation method, the enhanced image with higher quality can be generated.

A system debiases image translation models to produce generated images that contain minority attributes. A balanced batch for a minority attribute is created by over-sampling images having the minority attribute from an image dataset. An image translation model is trained using images from the balanced batch by applying supervised contrastive loss to output of an encoder of the image translation model and an auxiliary classifier loss based on predicted attributes in images generated by a decoder of the image translation model. Once trained, the image translation model is used to generate images with the minority image when given an input image having the minority attribute.

The present disclosure provides a visual stereoscopic enhancement method, device, equipment and storage medium, relates to the technical field of image processing, in particular to the technical field of image mask, image translation, image fusion and the like, and can be applied to the scenes of portrait visual stereoscopic enhancement, virtual digital person, image or video space sense enhancement and the like. The specific implementation scheme comprises the following steps: according to the configured light direction and light angle, a mask is translated by a target distance in a first mask image corresponding to a target object in a target image to obtain a second mask image; after the first mask image and the second mask image are subtracted pixel by pixel, the regions with a translation increment less than 0 and greater than 1 are discarded to obtain a shadow image; after the shadow image and the first mask image are added pixel by pixel, the target image is multiplied pixel by pixel to obtain an image after stereoscopic enhancement of the target object. The present disclosure can simply and quickly realize visual stereoscopic enhancement and improve visual stereoscopic sense.

An adaptable deep learning method is provided that delivers sound hepatic lesion identification in NETs, while significantly reducing human effort for data annotation and improving model generalizability for PET image quantification. A region-guided GAN (RGGAN) model conducts image-to-image translation between list-mode simulated PET images and real-world clinical data, while preserving semantic content of interest, e.g., lesions. The RG-GAN model is integrated with a lesion detection model into an end-to-end, unified framework for joint-task learning, such that the two models can benefit from each other. The RG-GAN translates the list-mode simulated data into real world-style images, which appear to be drawn from the real clinical PET image dataset, and feeds the translated images into the lesion detection model for training. In order to deal with the limited diversity of list mode-simulated PET image data, a specific data augmentation module is incorporated into the unified framework to improve model training.

The invention discloses an image translation-based inclined view angle rotator vibration displacement measurement method. The method comprises the following steps of: performing cutting operation on an acquired rotator vibration image under a standard view angle and an acquired rotator vibration image under L random view angles; randomly extracting a preset number of random view angle cutting images and standard view angle cutting images of matched frames from the L random view angles, and taking the standard view angle cutting images as labels of the random view angle cutting images to construct a data set; training and verifying the image translation model through the training set and the verification set to obtain a trained image translation model; selecting one of the obtained rotating body vibration images of the preset time length under the L random view angles, inputting the selected rotating body vibration images into a trained image translation model, and obtaining a standard view angle image of a frame number corresponding to the preset time length; carrying out image splicing operation according to the standard view angle images with the frame number corresponding to the preset duration to obtain a spliced image; and performing edge detection on the spliced image, extracting an edge contour of the rotating body, obtaining sub-pixel-level displacement data, and converting the sub-pixel-level displacement data into a displacement value under a world coordinate system. According to the invention, the vibration displacement of the rotor can be effectively and accurately extracted from a plurality of shooting angles, and a more reliable and accurate scheme is provided for the measurement of the vibration displacement of the rotating body.

Described herein are methods and systems for training image to image translation machine-learning models, wherein the image to image translation machine-learning models are trained using unpaired images. The trained image to image translation machine-learning models can be used to digitally identify markers of a fluorescent image depicting a plurality of markers in a single color channel from a single fluorophore and / or enhance a fluorescence image.

The application discloses a sketch progressive image translation method and system based on a conditional diffusion model, and the method comprises the following steps: inputting a to-be-processed sketch into a grayscale image generation model to obtain a sketch grayscale image, wherein the grayscale image generation model comprises a latent space unit and a conditional diffusion noise unit; performing feature extraction on the to-be-processed sketch and the sketch grayscale image respectively to obtain a sketch vector and a grayscale image vector; performing feature fusion on the sketch vector and the grayscale image vector to obtain image fusion feature information; and performing image translation on the image fusion feature information and the sketch grayscale image through a conditional diffusion model to obtain a sketch real image, wherein the conditional diffusion model comprises a forward diffusion unit and a reverse diffusion unit. The sketch feature is combined with the generated grayscale image, and then is added to the translation process from the grayscale image to the real image, so that the accurate understanding of the overall structure and the local feature of the sketch can be excellently maintained, and the quality of the image translation is improved.

A computer-implemented method of training a generator for transforming a given image according to a given target foreground domain is disclosed. A generator, discriminator, Mapping network, and a Style-Content encoder are trained. Furthermore, a method of image to image translation according to a given target foreground domain by the trained generator is provided. The generator may be trained to generate images depicting defects.

The application discloses a privacy protection heterogeneous federated learning method and system based on image translation, which are corresponding solutions, in the solutions: a generated model with local user data semantic retention capability is optimized through an image translation server, a local user performs data enhancement on a local heterogeneous data set by using the generated model, so that the local data presents a state of uniform distribution of class labels, and a classification model of the local user is trained by using transformed data and enhanced data, so that the global model accuracy and the privacy protection capability are balanced. Moreover, there is no interaction between the image translation server and the data classification server, and the image translation server is in a trusted computing environment, so that the privacy protection capability is ensured.

Methods and systems for pre-processing immunocytochemistry images for machine learning are provided. The pre-processing method includes receiving paired positive and negative multi-protein images and segmenting stains of the paired multi-protein images. The method also includes labelling each image pixel corresponding to a cell of the paired multi-protein images and translating image pixel information of the labelled image pixels to tabular form to generate a table of cell coordinates and geometrical characteristics for each of the paired multi-protein images. The method further includes generating two cell-paired tables for each of the paired multi-protein images based on Euclidean distance-based pairing prior to input for the machine learning, where the Euclidean distance-based pairing is based on the cell coordinates in the table of cell coordinates and geometrical characteristics for each of the paired multi-protein images.

This application discloses a data processing method and related apparatus, which enables the trained translation model to accurately generate descriptive information for an image to be translated, and to accurately translate the text to be translated contained in the image based on the descriptive information and the image to be translated. The descriptive information can accurately describe the image content in the image to be translated. By combining the descriptive information, the translation model can more accurately analyze and understand the image content, and thus more accurately analyze the translation method that should be selected under the image content corresponding to the image to be translated when translating the text to be translated. This makes the final translation result more consistent with the image content of the image to be translated, and improves the accuracy and rationality of image translation.

A system for unsupervised deep representation learning based on image translation is provided. The system includes an image translation transformation module used for performing a random translation transformation on an image and generating an auxiliary label; an image mask module connected with the image translation transformation module and used for applying a mask to the image after translation transformation; a deep neural network connected with the image mask module and used for predicting an actual auxiliary label of the image after the mask is applied and learning the deep representation of the image; a regression loss function module connected with the deep neural network and used for updating parameters of the deep neural network based on a loss function; and a feature extraction module connected with the deep neural network and used for extracting the representation of the image.

The present invention relates to a method for extracting ethnic costume sketches based on a cycle-consistent generative adversarial network, belonging to the field of image translation technology. The present invention utilizes a cycle-consistent generative adversarial network to automatically generate sketches of ethnic costume images, extracting edge and contour features of ethnic costume images to automatically generate ethnic costume sketches. Before input into the generator network, the source image undergoes bilateral filtering for noise reduction and color quantization to optimize the source image quality and facilitate the generation of the final ethnic costume sketch. The present invention can effectively and automatically generate ethnic costume sketches, achieving automatic generation of ethnic costume grayscale image sketches.

A few-shot, unsupervised image-to-image translation (“FUNIT”) algorithm is disclosed that accepts as input images of previously-unseen target classes. These target classes are specified at inference time by only a few images, such as a single image or a pair of images, of an object of the target type. A FUNIT network can be trained using a data set containing images of many different object classes, in order to translate images from one class to another class by leveraging few input images of the target class. By learning to extract appearance patterns from the few input images for the translation task, the network learns a generalizable appearance pattern extractor that can be applied to images of unseen classes at translation time for a few-shot image-to-image translation task.

The Abstract of the Disclosure is provided to allow the reader to quickly ascertain the nature of the technical disclosure. It is submitted with the understanding that it will not be used to interpret or limit the scope or meaning of the claims. In addition, in the foregoing Detailed Description, it can be seen that various features are grouped together in various examples for the purpose of streamlining the disclosure. This method of disclosure is not to be interpreted as reflecting an intention that the claims require more features than are expressly recited in each claim. Rather, as the following claims reflect, inventive subject matter lies in less than all features of a single disclosed example. Thus, the following claims are hereby incorporated into the Detailed Description, with each claim standing on its own as a separately claimed subject matter.

This invention discloses an unsupervised multimodal brain image translation method guided by tumor perception, comprising the following steps: using a U-Net-based neural network to construct a brain tumor image translation model suitable for translating between any two MRI modalities; the model comprises global and local branches for extracting and fusing feature information from the overall source image and the corresponding tumor image to generate an overall image and tumor image of the target modality, alleviating distortion issues in the generated image in the tumor region; and designing a teacher-student network architecture in which the student network learns the teacher network's knowledge and tumor perception capabilities through distillation, generating more realistic target modality images without relying on tumor label information. This method can generate high-quality target modality images using unpaired single-modality brain images, and does not require tumor label information during the testing phase, making it more consistent with real-world medical scenarios.

1. The name of the design product: scan translation pen (Q7 ultra). 2. The use of the design product: the design product is used for scanning text, image translation, and point reading learning. 3. The design points of the design product: in shape. 4. The picture or photo that best indicates the design points: perspective view 1.