91 results about "Hybrid image" patented technology

The invention discloses a monocular four-dimensional imaging device and method based on a metasurface. The monocular four-dimensional imaging device comprises an image sensor and an imaging optical system arranged in front of the image sensor. Wherein the imaging optical system comprises a metasurface, the metasurface is designed to simultaneously generate a self-acceleration Airy point spread function with depth-dependent lateral displacement for incident left-hand circularly polarized light and right-hand circularly polarized light, and the self-acceleration Airy point spread function can generate a self-acceleration Airy point spread function with depth-dependent lateral displacement under the condition that a mechanical moving part is not used. Carrying out polarization separation and focusing on the left-hand circularly polarized light and the right-hand circularly polarized light; the image sensor is used for recording a mixed image containing the left-hand circularly polarized light information and the right-hand circularly polarized light information at the same time through single exposure so as to obtain three-dimensional depth and polarization information of the metasurface, namely monocular four-dimensional imaging of the metasurface. The scene perception and understanding capability of a machine is greatly enhanced, and the method has unique advantages in the aspects of target identification, material discrimination, severe environment perception and the like.

Systems and methods for intraoperative tracking and visualization are disclosed. A current minimally invasive surgical (MIS) instrument pose may be determined based on a live intraoperative input video stream comprising a current image frame captured by a MIS camera. In addition, an instrument activation state and at least one parameter value associated with the instrument may also be determined. Intraoperative graphic visualization enhancements may be determined based on the activation state of the instrument, and / or a comparison of parameter values with corresponding parametric thresholds. The visualization enhancements may be applied to a current graphics frame. The current graphics frame may also include visualization enhancements related to proximate anatomical structures with proximity determined from the instrument pose and an anatomical model. The current graphics frame may be blended with the current input image frame to obtain an output blended image frame, which may form part of an output video stream.

The application discloses an open set image classification field self-adaption method based on self-step learning, first, the original image is preprocessed to obtain an image set, then a feature extraction module and a double multi-class classifier module are constructed and trained to align shared class features of source domain images and target domain images and separate target domain private class features, a multi-criteria cross-domain hybrid module is further constructed and trained, cross-domain hybrid images are generated by using the source domain images and the target domain images, and the shared class features are self-learned, and finally, a classification result of the target domain image is output. Compared with the existing open set image classification field self-adaption method, the application covers smooth and non-smooth class distribution, and does not need to empirically adjust the threshold for distinguishing common class images and private class images in the inference stage, so that the model has good robustness under different hyperparameters and experimental settings.

The invention discloses a quantum classical mixed image classification method based on group isovariant and delayed aggregation. The method comprises the following steps: lifting a convolution module, inputting a target image, initializing a convolution kernel and rotating the convolution kernel, and carrying out convolution and stacking on the target image to obtain an output image; the group convolution module is used for converting the output image of the convolution module into an image sample and carrying out convolution and reverse remodeling operation to obtain an output image; the group pooling and flattening module is used for carrying out adaptive average pooling on an output image of the group convolution module, compressing the output image into a feature vector and obtaining an output feature vector; the group equivariant quantum feature processing module encodes the feature vectors to quantum bits according to the feature vectors output by the group pooling and flattening module; and the delay aggregation and output module measures the quantum bits, aggregates and classifies measurement results, and completes classification of target images. The method provided by the invention has strong nonlinear feature fusion capability, and can realize more accurate image classification.

A device for unmixing images of samples with fluorescent dyes includes one or more hardware processors. The device is configured to obtain a mixed image of a sample; determine an unmixed image based on the mixed image; determine a noise-map image based on the mixed image; determine a signal-to-noise image based on the unmixed image and the noise map-image; determine a denoised signal-to-noise image based on the signal-to-noise image; and determine a noise-reduced unmixed image of the sample based on the denoised signal-to-noise image and on the noise-map image.

This disclosure describes systems, methods, and devices related to video post-processing using a single local directional pattern (LDP) for multiple post-processing steps. A method may include identifying video received by a first device from a second device and decoded by the first device; generating a LDP of a video frame of the decoded video; detecting, based on the LDP and the video frame input to a blurred region detection algorithm, a blurred region and a non-blurred region of the video frame; applying, based on the LDP and the video frame input to a super resolution algorithm, super resolution on the non-blurred region of the video frame without applying the super resolution to the blurred region; and generating, based on the LDP and the video frame input to a blended image algorithm, a blended image of a low-resolution image of the video frame and a high-resolution image of the video frame.

This application provides a multi-scale hybrid DR image enhancement method, apparatus, electronic device, and storage medium. The method includes: acquiring a target image to be identified; dividing the target image into N blocks according to N different block scales to obtain a first block image corresponding to each block scale; for each block scale, performing histogram equalization processing on each first block image corresponding to that block scale according to its corresponding contrast constraint to obtain a corresponding second block image; for each block scale, performing edge smoothing processing on each second block image corresponding to that block scale to obtain a corresponding third block image; and fusing all third block images corresponding to the N different block scales to obtain an enhanced image. This application solves the problem in related technologies where single local histogram equalization is difficult to simultaneously capture image details at different scales.

The invention belongs to the technical field of calculation, and relates to a mixed image data correction system and method, and the method comprises the steps: enabling a first industrial robot to run according to a preset path, continuously shooting the images of a lower road surface in the running process, obtaining the position information and illumination intensity of a marked region in each image, and obtaining the position information and illumination intensity of the marked region; generating standard image information corresponding to the preset path; the first industrial robot sends the standard image information to an upper computer, and the standard image information is synchronized to all the industrial robots through the upper computer; after the industrial robot starts to work, the industrial robot runs according to a preset path, continuously shoots images of a road surface below the industrial robot in the running process, obtains position information and illumination intensity of a marked area in each image, and compares the position information and the illumination intensity with corresponding standard image information; whether driving deviates or not and whether the environment illumination intensity changes or not are judged, so that the calculation power needed by the industrial robot during judgment and comparison is reduced, and the comparison time is shortened.

This disclosure provides systems, methods, and devices for image signal processing that support improved demosaicing of color image signals. In a first aspect, an image processing method includes receiving a first image frame and a second image frame. The method may also include determining a first demosaiced image frame by applying a first demosaicking process to the first image frame, and determining a second demosaiced image frame by applying a second demosaicking process to the first image frame based on the second image frame. A blending weight may be determined based on the first image frame and the second image frame, and a blended image frame may be determined by combining pixel values from corresponding portions of the first demosaiced image frame and the second demosaiced image frame according to the blending weight. Other aspects and features are also claimed and described.

A system for producing an image to design a product can include a processor and a memory. The memory can store a regularizing module, a blending module, a denoising module, and a communications module. The regularizing module can produce a regularized image of a denoised image of an interpolation of a first diffused image and a second diffused image. The regularized image can be regularized with respect to a visual pattern. The blending module can: (1) determine a blending weight and (2) produce, based on the blending weight, a blended image of the denoised image and the regularized image. The denoising module can denoise the blended image to produce the image to design the product. The communications module can cause the image to be sent to a computer-aided design system to design the product.

This invention provides a method, apparatus, terminal device, and medium for hybrid oversampled image enhancement, including: acquiring an original image dataset; constructing a class-reweighted distribution based on the number of effective samples in each class; sampling foreground image samples from the class-reweighted distribution and background image samples from the long-tail distribution; segmenting the foreground and background image samples into non-overlapping image blocks to obtain a set of foreground image blocks and a set of background image blocks; generating a binary mask for each image block; performing element-wise mixing of the foreground and background image block sets to obtain hybrid image samples; extracting neural activation maps of the foreground and background image samples; and using the neural activation maps to assign content-aware soft labels to the hybrid image samples. This invention can improve the recognition accuracy and generalization robustness of Transformer-based visual recognition models for tail categories in long-tail distribution scenarios.

The application discloses a distributed X-ray security inspection machine image judgment system and method. A mode selection unit determines an image judgment mode according to the number of packages passing through an X-ray security inspection machine within a preset time length. In the self-image judgment mode, an image acquisition unit acquires an initial X-ray image sequence, and a local image processing unit processes the initial X-ray image sequence to obtain a pseudo-color X-ray image sequence, and an image judgment display unit obtains an image judgment result. In the hybrid image judgment mode or the external image judgment mode, the image acquisition unit sends a specified initial X-ray image in the initial X-ray image sequence to a cloud image processing unit, the cloud image processing unit processes the received specified initial X-ray image to obtain a pseudo-color X-ray image, and the image judgment display unit obtains the image judgment result. The distributed X-ray security inspection machine image judgment system provided by the application improves the balance of image judgment work, and each X-ray security inspection machine can complete on-demand image judgment, thereby further improving the image judgment efficiency compared with the scheme of centralized personnel image judgment.

The invention discloses a tray detection method in an industrial changeable environment based on domain self-adaption, and the method comprises the steps: taking a label-containing tray image data set corresponding to an original tray detection model in an original scene as a source domain data set, and taking a label-free tray image data set in a deployment scene as a target domain data set; performing style migration on the source domain data set and the target domain data set by adopting an image style conversion model to generate a class target domain style data set and a class source domain style data set, randomly intercepting sub-region images from corresponding images in the source domain data set and the class target domain style data set, and splicing the sub-region images into a mixed image to obtain a mixed domain data set; constructing a domain adaptive tray detection model, and training the domain adaptive tray detection model; and calculating a loss function of the student model and the teacher model, and minimizing the loss function to obtain the trained teacher model and student model for tray detection. According to the invention, the problem of domain offset caused by environment change can be effectively relieved.

The invention relates to the technical field of coal gangue recognition, in particular to a coal gangue classification recognition method and system based on image vision, and the method comprises the steps: obtaining a mixed image of coal and coal gangue through employing a camera array, and dividing a plurality of candidate regions; based on the overlapping position between the candidate areas, the mixing state of the coal and the coal gangue on the overlapping position is determined, and feature loss is used for distinguishing the sheltered area and the unsheltered area under coal gangue mixing; on the basis of continuous frame images of the occlusion area, synchronizing feature loss of the occlusion area to an adjacent non-occlusion area, and determining a spatial similarity threshold value of the adjacent non-occlusion area; performing multi-target cooperative processing according to the spatial position of the sheltered area, and unifying classification labels of coal gangue in each candidate area; and defining region features of each candidate region according to the value range of the classification tag, and taking the region features as synchronously output data. The accuracy and efficiency of coal gangue recognition are realized.

The invention provides an image processing method, a camera and electronic equipment. The camera comprises a hybrid image sensor and an optical lens, the hybrid image sensor is located on the image side of the optical lens to receive light emitted from the optical lens, the hybrid image sensor comprises first pixels and second pixels, any first pixel can independently obtain light intensity changes and output event signals, and any second pixel can independently obtain light intensity changes and output event signals. The number of color channels of the second pixel is greater than or equal to 7. According to the camera provided by the invention, through the design of the hybrid image sensor, the functions of an event camera and a multispectral camera are integrated into the camera, lens holes of the electronic equipment are reduced, and the overall size design of the electronic equipment can be prevented from being interfered.

The application discloses a line illumination modulation dichromatic tomography imaging system, belonging to the technical field of multicolor tomography imaging, comprising: a line illumination modulation module, comprising two monochromatic light sources and a modulation light path, each light beam is modulated to form a line light spot focused on the focal plane of an objective lens, the position parameter of the line light spot is located on the center of a pixel row or the intersection line of adjacent pixel rows; the distance between the position parameters of the two line light spots is an integer multiple of 0.5 pixels; an imaging module, a multi-element detector with n rows of pixels is used to continuously scan imaging along a first direction, n mixed images are obtained, each mixed image corresponds to a row of pixels, each mixed image contains the signals of 2 channels, n is a positive integer; an image demodulation module is used to subtract the mixed images of two symmetrical pixel rows, the signals of one channel are eliminated from the mixed images, monochromatic images are demodulated, and the monochromatic images contain the signals of another channel. The application realizes synchronous dichromatic tomography imaging on a single detector.

The present disclosure describes an image system including a hybrid image sensor and control circuitry for image deblurring by complementary metal oxide semiconductor (CMOS) image sensor (CIS)-event vision sensor (EVS) fusion. The hybrid sensor includes an event driven sensing array having a plurality of EVS pixels and a pixel array having a plurality of CIS pixels. The EVS pixels capture contrast data during a first time interval, while the CIS pixels capture light intensity data during second and third time intervals. The control circuitry deblurs the first CIS data using the EVS data, generates fusion masks and weights based on the EVS and CIS data, and fuses the deblurred and subsequent CIS data using the masks and weights. The second time interval occurs prior to the third time interval.

The application discloses a quantum-classical hybrid image classification method based on group homomorphism and delay aggregation, which comprises the following steps: a lifting convolution module is used to input a target image, initialize a convolution kernel and rotate, and then the target image is convolved and stacked to obtain an output image; a group convolution module is used to convert the output image of the convolution module into an image sample, and then convolution and inverse reshaping operations are performed to obtain an output image; a group pooling and flattening module is used to perform adaptive average pooling on the output image of the group convolution module, and the output image is compressed into a feature vector to obtain an output feature vector; a group homomorphism quantum feature processing module is used to encode the feature vector output by the group pooling and flattening module into a quantum bit; and a delay aggregation and output module is used to measure the quantum bit, aggregate and classify the measurement results, and complete the classification of the target image. The method has strong nonlinear feature fusion capability and can realize more accurate image classification.

Systems and methods for enhancing images by blending infrared and optical features includes an infrared camera capturing infrared images and an optical color camera capturing optical images of the same scene. A processor executes object detection on both image types, extracts features from detected objects, and compares these features. Based on the comparison, the processor assigns a blending percentage that includes proportions of infrared and optical features. The system outputs a blended image using the assigned blending percentage. The system improves visibility and object detection in low-light conditions, enhancing overall driving safety.

This invention discloses a method for detecting cracks in underwater concrete structures based on deep learning and artificial intelligence. The method includes: annotating cracks in a mixed image dataset using a labeling tool and defining crack category labels to obtain an image sample dataset; performing image enhancement processing on the image sample dataset based on a constructed image enhancement strategy; performing image preprocessing on the feature enhancement feature maps in the feature enhancement dataset to obtain preprocessed images and extracting crack image features to obtain multi-dimensional feature vector feature maps; improving the original YOLOv13 network model to construct an underwater concrete structure crack detection model; and training the constructed underwater concrete structure crack detection model using the enhanced feature maps to obtain the optimal crack detection model for detecting cracks in underwater concrete structures. This invention solves the technical problem of existing methods being unable to effectively detect cracks in underwater concrete structures due to the scarcity of real samples.

The application provides a unified recognition method, system, storage medium and equipment for text images and formula images, and belongs to the technical field of computer vision and natural language processing. The application explicitly encodes the document hierarchy by inserting line end markers and paragraph end markers into the training label sequence; and constructs a semantic decoupling segmenter to assign independent identifiers to the same symbols in the text and the formula, thereby solving the semantic confusion problem. After extracting image features based on a visual encoder, autoregressive training is performed in combination with a decoder to achieve high-precision unified recognition of text, formula and mixed images. The application achieves recognition accuracy comparable to large-scale models at only 0.1B parameter scale, significantly improves the inference speed, and can be widely used in document digitization, academic literature processing and other scenes.

A first aspect of the present disclosure relates to a device for unmixing an image of a sample having fluorescent dyes.SOLUTION: The device is configured to obtain a mixed image of the sample, determine an unmixed image based on the mixed image, determine a noise map image based on the mixed image, determine a signal-to-noise ratio image based on the unmixed image and the noise map image, determine a denoised signal-to-noise ratio image based on the signal-to-noise ratio image, and determine a noise-reduced unmixed image of the sample based on the denoised signal-to-noise ratio image and the noise map image.SELECTED DRAWING: Figure 1

The application discloses a line illumination modulation multicolor imaging system, belonging to the technical field of multicolor imaging, comprising: a line illumination modulation module, comprising m monochromatic light sources and a modulation light path, the wavelength of the light beam emitted by each monochromatic light source is different; each light beam passes through the modulation light path to form a line light spot focused on the focal plane of an objective lens, the illumination intensity of the line light spot on the focal plane of the objective lens is in Gaussian distribution in a first direction, the position parameters of each line light spot are different, and the line light spots of the m channels are superimposed to form m-color line illumination light; an imaging module is used for continuously scanning imaging along the first direction by using a multi-element detector with n rows of pixels, and at least m mixed images under the irradiation of the line illumination light are obtained, each mixed image corresponds to one row of pixels, and each mixed image contains signals of the m channels; and an image demodulation module is used for demodulating monochromatic images from the mixed images, and each monochromatic image corresponds to the signals of one channel. The application realizes synchronous multicolor imaging on a single detector.

As one aspect disclosed herein, an image processing method may be proposed. The method is executed in an electronic device comprising one or more processors and one or more memories for storing instructions to be executed by the one or more processors, and may comprise the steps of: acquiring a plurality of mixed images of a sample including a plurality of biological molecules; and generating unmixed images of at least one of the plurality of biological molecules from the plurality of mixed images by using an unmixing matrix. The value of at least one element included in the unmixing matrix may be determined on the basis of artificial neural network model training.

A method includes obtaining, using at least one processing device of an electronic device, raw image frames of a scene. The raw image frames include different sets of raw image frames captured at different viewpoints and different viewing angles relative to the scene. The method also includes performing, using the at least one processing device, blending of each set of raw image frames in order to generate blended image frames of the scene. The method further includes training, using the at least one processing device, a machine learning model using the blended image frames. The machine learning model is trained to generate three-dimensional (3D) information about the scene from viewpoints and viewing angles not captured in the sets of raw image frames.

The application discloses a transformer key component effective sample expansion method based on reduction knowledge, and comprises the following steps: A, constructing an image data set of a transformer key component; B, inputting the image data set into a semantic enhancement network ISDA to perform image data expansion; C, creating an image compensation network based on a guided back propagation, and compensating image data expanded in the step B; and D, mixing the image data set compensated in the step C with a real inspection image data set to obtain a mixed image data set, and completing effective sample expansion. The application can improve the defects of the prior art, effectively increase the richness and diversity of transformer component image samples, and meet the requirements of a detection algorithm from a sample level.

Various systems and methods are provided for generating a blended image including harmonic image data from a harmonic image and fundamental image data from a fundamental image. An ultrasound probe may be controlled to acquire a fundamental image of a region of interest of a subject and a harmonic image of the region of interest of the subject. A transition zone at which to blend the harmonic image and the fundamental image may be determined. A blended image including harmonic image data from the harmonic image and fundamental image data from the fundamental image may be generated based on determining the transition zone. A display may be controlled to display the blended image.

The invention provides a texture flow control method and apparatus, and an electronic device. The method comprises the steps of generating a first layer and a second layer based on a target texture map; in response to a path drawing operation acting on the target texture map, generating two curve paths in the target texture map based on the path drawing operation; based on the two curve paths, the texture flowing direction is determined, a path masking map is generated, and a display area in the path masking map is an image area between the two curve paths; on the basis of the texture flowing direction and a preset time parameter, respectively configuring displacement changing along with time for the first image layer and the second image layer, and respectively carrying out displacement processing on the first image layer and the second image layer on the basis of the displacement to obtain the first image layer and the second image layer after displacement processing; and mixing the path mask pattern, the first pattern layer after displacement processing and the second pattern layer after displacement processing to obtain a mixed image of texture flow change in the display area. According to the mode, the texture flowing direction and the texture flowing area are accurately controlled through the two curve paths.

A view angle switchable display apparatus includes: a timing control unit that generates shared image data, private image data, a data control signal, and a gate control signal from interface signals corresponding to mixed image data, a data enable signal, a shared data enable signal, and a private data enable signal received from an image processing unit; a data driving unit configured to generate a shared data signal and a private data signal using the shared image data, the private image data, and the data control signal; a gate driving unit configured to generate a gate signal using the gate control signal; and a display panel including a shared sub-pixel and a privacy sub-pixel, and configured to display an image using the shared data signal, the privacy data signal, and the gate signal.