25 results about "Ideal image" patented technology

The invention discloses a low-illumination visible light and infrared image fusion method with color recovery capability, and belongs to the technical field of computer vision. The method comprises the following steps: designing a low-light enhancement loss function based on comparative learning, so that the color distribution of an enhanced visible light image is aligned with the color distribution of an ideal image; designing a low-light enhancement neural network based on a Mangbar structure, converting the low-illumination image into a YCbCr color space, decoupling an illumination component and a color component of the low-light image, and respectively implementing enhancement processing; a fusion network based on a Mangban structure is designed, modal complementary information is obtained through a mixed attention mechanism, the enhanced visible light image and the infrared image are fused, color information of the fused image can be effectively recovered, and the visual quality of the fused image is improved.

The invention discloses a refraction distortion automatic correction method and system for an SPIV experiment, and belongs to the technical field of fluid mechanics experiment measurement, and the method comprises the following steps: obtaining a camera shooting posture and medium physical parameters; constructing a multi-layer medium forward refraction model; iteratively adjusting the assumed real angle and repeatedly calling the forward model by taking the measured distortion angle as a target through reverse iterative calculation until the predicted angle of the model is matched with the measured angle, and solving the corrected actual camera angle; constructing a nonlinear mapping relation from a distorted image coordinate to a non-refraction ideal image coordinate according to the difference between an actual angle and a measurement angle; performing reverse geometric transformation and interpolation on the original SPIV image by using the mapping relation to generate a correction image for eliminating the refraction effect; according to the invention, a software algorithm is used for replacing a traditional water prism physical correction device, so that the cost and complexity are reduced, and the adaptability to different experimental conditions and the correction precision are improved.

An information processing device (110) comprises: an optical calculation unit (112) that calculates each of the optical path of light from an imaging field of view in an imaging device that performs imaging using an optical element and the optical path of light from outside the imaging field of view that is outside the imaging field of view, calculates stray light produced by the optical element and a point spread function (PSF) of the optical element, and generates a degraded image in which the stray light and the PSF are applied to an ideal image that is handled as correct answer data in teacher data; a noise image generation unit (113) that generates a noise image including noise predicted to occur in an imaging element of the imaging device during imaging; an image calculation unit (114) that superimposes the noise image on the degraded image to generate a learning image; and a teacher data generation unit (115) that generates teacher data in which the learning image is employed as training data and an image corresponding to the imaging field of view of the imaging device within the ideal image is employed as correct answer data.

The invention discloses a phase difference method wavefront detection method combined with image sensor noise suppression, and the method comprises the steps: building an ideal light spot image of an image surface and a defocus position based on a phase difference method phase recovery system diffraction model; calculating a noise suppression factor of each defocus position according to the average noisy image of each defocus position and the image sensor parameters, and applying the noise suppression factor to the corresponding average noisy image to obtain a corrected noisy image of each defocus position; and finally, substituting the ideal light spot image at each out-of-focus position and the corrected noise-containing image into a target function, optimizing through a nonlinear algorithm, repeatedly updating the Zernike coefficient and recalculating the ideal image, and finally obtaining a restored wavefront phase. According to the invention, the wavefront detection precision can be significantly improved.

An object is to obtain training data through an ideal image generated from an actually-shot image.A data generating method of generating data used in learning a model using a processor, the data generating method including: generating an ideal image corresponding to output data of the model from a captured image obtained by a predetermined device; and generating a degraded image corresponding to input data of the model from the ideal image.

An object of the system according to the embodiment is to allow a user to specifically imagine an ideal user himself / herself and to match the user with another person on the basis of the image.SOLUTION: A system includes an image generation part, a matching part, an appointment setting part, and a consulting part. The image generation unit generates an ideal image of the user based on the photograph of the user. The matching unit performs matching between the ideal own images. The promise setting unit sets a promise to actually meet when the matching is established. The consulting part provides consulting for approaching an ideal self for an appointment.SELECTED DRAWING: Figure 1

The application discloses an unsupervised pedestrian re-identification method based on camera perception graph learning. Firstly, pedestrian image features are extracted, pseudo labels are obtained by using a clustering algorithm, and an in-memory dictionary is constructed. Each camera is regarded as an independent image domain, and samples are extracted from each image domain to form small batch training samples. Then, all image sample features are used to construct a camera perception graph, image samples are fused into a unified feature space by using a graph convolution network, and ideal image features are generated by feature aggregation. A non-parametric contrast loss based on the in-memory is constructed to train the feature extractor and the graph convolution network, and a camera adversarial loss is constructed to generate more robust and effective image features. The foregoing steps are repeatedly executed until the model converges. Finally, the pedestrian image is mapped into a feature space by using the feature extractor, a feature vector of a to-be-queried pedestrian image is compared with feature vectors of candidate pedestrian images in terms of Euclidean distance, and the pedestrian re-identification result is obtained.

The invention relates to the technical field of digital image processing, and discloses a digital image detector and method, and the detector comprises an image collection module which is used for obtaining original digital image data; the calculation processing module is used for processing the image, and the processing comprises the steps of correcting macroscopic geometric deformation of the image; analyzing the image by using a pre-trained generative model to identify microscopic content defects, and generating an ideal image and a structure residual image; the ideal image is used as semantic guidance, and geometric regularity and material uniformity of the image are evaluated; finally, various analysis results are fused, a comprehensive quality score is calculated, and a comprehensive defect positioning map is generated. And the output feedback module is used for outputting the comprehensive quality score and the comprehensive defect positioning map. According to the method, a physical and structural analysis two-way collaborative semantic guidance detection mechanism is constructed, so that accurate, comprehensive and objective automatic comprehensive evaluation on the digital file image quality is realized.

The invention relates to the technical field of local dimming, and discloses a local dimming method, which comprises the following steps: image acquisition and data processing: a PC host controls a PG tester to output a standard test picture to a display panel, photographs and captures an actual display image of the display panel, and a system compares an ideal image with the actual picture to generate an original Mura data matrix; compression compensation data generation: fitting a Gamma curve of the display panel, based on the fitted Gamma curve and the original Mura data matrix, calculating to generate a precise compensation coefficient per pixel or partition, and compressing the compensation data; and burning the data and verifying the effect. Compared with a traditional module, a layer of black and white panel and a control panel are additionally arranged on hardware, a local dimming program is integrated on the control panel, and local dimming of more dimming partitions can be achieved by applying a smaller number of LEDs.

The present application relates to the technical field of rectal cancer CTV segmentation, in particular to a rectal cancer CTV segmentation method based on CViL-UNet, comprising the following steps: preprocessing rectal cancer CT image data, converting a 3D CT sequence into a multi-channel 2D input format, removing image noise and intensity non-uniformity, and standardizing the pixel value range to adapt to the CViL-UNet model input. The present application realizes accurate identification of the key areas of CTV primary tumor, subclinical spread area and micro-infiltration focus through local-global feature cooperation and high-frequency detail enhancement, solves the pain points of missing micro-infiltration and misjudging healthy tissue in traditional methods, can process conventional CT images without intestinal preparation, breaks the dependence of traditional methods on ideal image conditions, and the automatic and real-time segmentation process in clinical value eliminates the subjectivity and inefficiency caused by manual intervention, while meeting the dual requirements of millimeter-level precision and rapid deployment for radiotherapy planning.

A three-dimensional (3D) scanning system includes a projector that generates an emitted light projected on an object, a reflected light being reflected from the object; a sensor that generates image data according to the reflected light; and a depth processor that generates depth data according to the image data and at least one modified factor representing corresponding deviation amount between the image data and an ideal image data due to a circle of confusion caused by the reflected light passing a lens of the sensor and then irradiating a sense plane of the sensor out of focus.

The invention relates to the technical field of optical data processing, in particular to an optical image simulation and data set automatic generation method, which comprises the following steps of: firstly, shooting an original image; taking a zero-aberration point spread function as an Airy spot convolution kernel, and performing convolution operation on the shot original image by using the Airy spot convolution kernel to obtain an ideal image reflecting a diffraction limit condition; taking the point spread function of the specified aberration as an aberration convolution kernel, and performing convolution operation on the shot original image by using the aberration convolution kernel to obtain a simulated aberration image; and synchronously cutting the obtained ideal image and the simulation aberration image to obtain a simulation data set. According to the method, a zero-aberration point spread function is used as an Airy spot convolution kernel, a point spread function of a specified aberration is used as an aberration convolution kernel, parameterized convolution is carried out on an original image, and an ideal-aberration strict paired image is efficiently obtained; through consistent cutting and unified file storage mapping, standardized organization and traceable management are realized.

The application discloses a kind of for the correction method and device of cone beam CT system geometry parameter error, method includes: acquisition projection image projected on flat panel detector;Obtain the geometry parameter error of cone beam CT system;By the translation error of transverse and longitudinal of the plane where flat panel detector is, correct projection image, obtain first correction image;The normal rotation error of the plane where flat panel detector is and the distance error between flat panel detector and ray source are corrected to first correction image, obtain second correction image;For second correction image, respectively correct along the rotation error of transverse and longitudinal of the plane where flat panel detector is, obtain third correction image;The distance error of third correction image's rotating table and ray source is corrected, obtain the ideal image of geometry parameter error correction.By geometry parameter error correction projection image, without adjusting the position of cone beam CT system device, it is easy to calculate, effectively eliminate the influence of system error on image three-dimensional reconstruction.

The application relates to a method and device for eliminating crosstalk between image sensor pixels. The method comprises: establishing a crosstalk convolution mathematical model between an actual image function and an ideal image function of the image sensor pixels, and obtaining regular filter coefficients based on a convolution kernel in advance to obtain crosstalk coefficients and store the same; obtaining an actual image function collected by the image sensor; and performing convolution calculation on the crosstalk convolution mathematical model according to the actual image function and the crosstalk coefficients to obtain an ideal image function in which crosstalk between pixels is eliminated and output the same. The application converts the crosstalk problem between pixels into a convolution problem by establishing the crosstalk convolution mathematical model. For the image sensor under the same process condition, the same convolution kernel can be used to eliminate the crosstalk between pixels and restore the ideal image by the regular filter without changing the pixel structure.

An object of a system according to an embodiment is to provide a specific action guideline and mental attitude for a user to approach an ideal person image.SOLUTION: A system includes a character selection part, a feature analysis part, and an action guide provision part. The character selection component accepts a character selected by the user. The characteristic analyzer analyzes the characteristics of the character received by the character selector. The action guideline providing unit provides a specific action guideline and mental attitude based on the characteristics of the character analyzed by the characteristic analyzing unit.SELECTED DRAWING: Figure 1

The invention discloses an optical part assembly correction method and computer equipment, and the method comprises the steps: obtaining an actual image through a camera disposed at a set observation point after the assembly of an optical part is completed; calculating the actual imaging distortion parameter of the current assembly based on the comparison between the actual imaging and the ideal imaging; inputting the determined actual imaging distortion parameter into a trained neural network to obtain a corresponding actual assembly error; and comparing the actual assembly error with a target assembly deviation corresponding to the target optical distortion, and correspondingly correcting the assembly of the optical part. According to the invention, how to correct the assembly of the optical part can be quickly and accurately determined at one time, the assembly efficiency is improved, and the subsequent optical quality is ensured.

The invention discloses a pig backfat thickness intelligent measurement method based on artificial intelligence deep learning, and relates to the technical field of application of computer vision and artificial intelligence in agricultural breeding, and the method comprises the following steps: S1, collecting an original image of a pig back area; s2, inputting the original image into an image quality evaluation module; according to the pig backfat thickness intelligent measurement method based on artificial intelligence deep learning, a closed-loop processing mechanism of image quality perception and multi-round iteration verification is introduced, the applicability and accuracy of pig backfat thickness measurement in a real complex scene are effectively improved, the system can automatically recognize and adapt to different types of image degradation problems, and the system is high in practicability and high in practicability. Through targeted image enhancement and a multi-stage measurement process, the excessive dependence of a single model on ideal image quality is overcome, so that the stable measurement performance can still be kept under the conditions of insufficient illumination, noise or motion blur and the like.

This invention discloses a dynamic distortion correction method and imaging system for imaging systems, relating to the field of imaging. The invention generates a distortion matrix by collecting difference data between a calibration image and an ideal image and performing mapping using polynomial fitting. Based on these matrices, a pre-distorted image can be obtained and its optical field synthesized and encoded. Finally, a multi-directional consistent distortion-free optical field is formed through a light control component. This invention effectively cancels optical path distortion, supports multi-angle and multi-dimensional dynamic distortion correction, and significantly improves the readability, stability, and consistency of displayed content. Through this technology, users can obtain a more accurate and stable visual experience, significantly improving the performance of existing imaging systems.

The invention provides an extrapolation area-array camera distortion correction method and system based on local measurement data, and the method comprises the steps: adjusting a two-dimensional rotary table to enable a simulation image point to be imaged at a preset measurement point in a central position A region of a focal plane of an area-array detector, locking the two-dimensional rotary table, and measuring the rotation angle of the two-dimensional rotary table of a module M through an autocollimator; acquiring image point coordinates by using data acquisition and control equipment; adjusting the two-dimensional rotary table to enable the simulation image point to be imaged at a preset measurement point of the quadrant III in the focal plane of the area array detector, and measuring the rotation angle of the two-dimensional rotary table; collecting image point coordinates; obtaining main point and main distance estimation values of an area-array camera optical system by using a least square method theory according to the central position A area measurement data; obtaining a theoretical value corresponding to a quadrant III sampling point according to the quadrant III sampling point data and the main point and main distance estimation values; utilizing two-dimensional curved surface fitting to express a geometrical relationship between ideal image points and image points influenced by distortion; and carrying out camera distortion correction based on a geometrical relationship between an ideal image point and an image point influenced by distortion in combination with symmetry and uniformity of area array detection.

The present application provides an image dynamic range test method, which comprises: obtaining an image sample set, the image sample set comprising a set of images collected under simulated scenes and real scenes; calculating image data of the image sample set to obtain a plurality of different ideal image index values; collecting corresponding images by using a target device to obtain a to-be-tested image; calculating image data of the to-be-tested image to obtain a plurality of different to-be-tested image index values; and performing one-to-one comparison and calculation between the plurality of different ideal image index values and the plurality of different to-be-tested image index values to obtain a dynamic range score of the to-be-tested image. In addition, the present application also provides a computer device for implementing image dynamic range test and a storage medium.

The invention provides a textile defect intelligent classification and source tracing method based on machine vision, and relates to the technical field of textile defect identification, and the method comprises the following steps: repairing a target region containing a defect region to obtain an ideal image when the target region has no defect, and determining the attention weight of each pixel point in the target region; dividing the target area image into a high-attention sub-area image, a middle-attention sub-area image and a low-attention sub-area image based on the attention weight; and carrying out differential analysis on each sub-region image based on the defect classification model to output the defect type of the target region image, and carrying out process tracing based on the defect type. According to the method, feature extraction of different convolution depths is carried out on the sub-region images with different attention degrees, so that the accuracy of defect classification based on machine vision is guaranteed, the model training efficiency is remarkably improved, and waste of meaningless computing resources is greatly reduced.

The invention discloses a garage water seepage intelligent early warning system and method based on machine vision, and relates to the technical field of machine vision, and the method comprises the steps: presetting an acquisition period, carrying out the format conversion, noise reduction and identification processing of image data, filtering a plurality of light interference factors, extracting multi-dimensional visual features, and constructing a depth image recognition learning model; the image data availability and accuracy under complex illumination are improved; dynamically adapting to light fluctuation by using an image feature gradual change curve graph, depth model prediction and weighted fusion, and accurately capturing an environment change trend; a generative adversarial network is introduced to simulate an ideal image, a deviation monitoring threshold value is set based on a three-sigma principle, and image abnormity caused by light interference is quantized. Compared with a traditional mode, the method breaks through the restriction of light interference on image analysis, achieves intelligent water seepage early warning, enhances the adaptability of the system to a complex environment, and effectively improves the reliability and safety of the garage monitoring system.

This invention discloses an underwater robot capable of underwater pipe cleaning and leak detection, belonging to the field of robotics. It solves the problem of low efficiency in existing technologies that use cameras to capture pipe images for leak analysis after cleaning, requiring the water to become clear before ideal images can be acquired. Furthermore, if the leak point contains colored liquid, useful image information may not be obtained. This invention includes a frame, a control system mounted on the frame, a thruster, a buoyancy chamber, and a camera mounted on the frame and connected to the control system. It also includes a cleaning component connected to the control system for cleaning the pipe and a leak detection component for pressure sensing and leak detection of the cleaned pipe. It is used for pipe cleaning and leak detection.

According to a method for autofocusing on a microscopic sample, measurement light having a local structure is generated. The measurement light is coupled into the microscope beam path, whereby the measurement light is incident on, and reflected by, the sample. The measurement light reflected by the sample is output from the microscope beam path and split among a plurality of component beam paths which pass over optical paths of different lengths to image the reflected measurement light, whereby a plurality of measurement images assigned to different focus positions on the microscope beam path are obtained. At least the measurement image which comes closest to an ideal image of the local structure of the measurement light is selected. Depending on the focus position assigned to the selected measurement image, a focus position to be used on the microscope beam path for the purpose of microscopic imaging of the sample is set.

The invention relates to the field of integrated circuit production, in particular to a mask defect detection method, device and equipment and a storage medium. Performing optical simulation on the to-be-detected mask under a preset ideal imaging condition to obtain an optical simulation image; comparing the optical simulation image with standard-exceeding pixel points of the optical detection image; obtaining the ratio of the standard-exceeding pixels of the optical simulation image; judging whether the ratio of the standard-exceeding pixels exceeds a preset condition switching threshold value or not; performing optical simulation on the to-be-detected mask again under a preset error imaging condition to obtain a corresponding error simulation image, and comparing the error simulation image with standard-exceeding pixel points of the optical detection image; and determining the defect position of the to-be-detected mask according to the standard-exceeding pixel points of the error simulation image. According to the invention, the interference of detection system hardware is eliminated, the signal-to-noise ratio is improved, and the detection accuracy and the robustness of the detection system are improved.