28 results about "Blind deconvolution" patented technology

The invention relates to the technical field of medical image processing, in particular to a digestive endoscopy image deblurring enhancement method and system.The method comprises the steps that firstly, an input digestive endoscopy original image is processed through a blurred region classification network, and a pixel-level blurred classification map capable of distinguishing an adhesion blurred region and a motion blurred region is generated; then, parallel processing is carried out according to the classification graph: for an adhesion fuzzy region, physical model restoration and color correction are carried out by estimating a transmissivity graph and an ambient light value; for a motion blur region, a self-adaptive non-blind deconvolution kernel is constructed to perform deconvolution sharpness. And finally, inputting the two processing results and the original clear area into a multi-scale feature fusion network together, carrying out adaptive feature weighted fusion and image reconstruction, and outputting a globally clear and detail-enhanced final image. According to the method, accurate identification and targeted enhancement of composite blurring are realized, and the visual quality and diagnosis availability of the digestive endoscopy image are effectively improved.

The present application relates to the adaptive signal denoising decomposition method for mechanical transmission system compound fault identification, belongs to the mechanical vibration signal processing and fault diagnosis technical field. Adopting the autoregressive model to remove the harmonic component of vibration signal adaptively, through the construction filter group decomposition AR denoising vibration signal, obtains a series of modes, estimates the fault period of mode, and with the help of blind deconvolution theory, adaptive iteration updates filter and mode, carries out mode selection according to multidomain correlation coefficient and fault period consistency coefficient, determines the optimal filter length according to the weighted square envelope harmonic noise ratio, carries out square envelope spectrum analysis to the optimal mode obtained after adaptive denoising decomposition, and finally realizes compound fault identification. The present application can carry out adaptive denoising decomposition to the vibration signal of mechanical transmission system, does not need to construct priori base function and fault period priori knowledge, and significantly improves the accuracy of compound fault identification.

The invention discloses an image deblurring method and device based on blind deconvolution, and relates to the technical field of engine turbine blade strain measurement image deblurring. In order to solve the defect that the dual requirements for image quality and processing efficiency in engine blade strain detection are difficult to meet in the prior art, the technical scheme provided by the invention comprises the following steps: acquiring images of a blade in a static state and at different rotating speeds, taking a static image as a reference, and taking a dynamic image as a to-be-processed image; initializing a blind deconvolution parameter based on a Gaussian blur kernel; performing blind deconvolution iterative optimization under the constraint of a still image, and jointly estimating a clear image and a blurred kernel to obtain a preliminary deblurred image; de-noising is carried out; and adaptively selecting a gamma value according to the histogram mean value difference between the de-noised image and the still image, and executing gamma correction to enhance the contrast and details. According to the method, a deblurring-denoising-enhancing link is formed, the image definition can be effectively recovered under the condition of high-speed rotation, and the strain detection precision and stability are remarkably improved.

The invention provides a target signal and underwater acoustic channel decoupling method and system based on a conditional generative adversarial network, and the method comprises the steps: inputting a preprocessed receiving signal and condition information into a trained neural network model, and outputting an estimated channel response; deconvolution is carried out on the estimated channel response and the received signal to obtain a source signal estimation value, and decoupling of the target signal and the underwater acoustic channel is achieved; the neural network model is a conditional generative adversarial network. Compared with a traditional blind deconvolution method, the method has the advantages that the channel decoupling capacity is higher, and decoupling of signals and channels can be more effectively achieved; compared with the prior art, the method is higher in robustness, can generate target signals with consistency and authenticity in different complex environments, supports the continuous updating and optimization of the model according to the actual deployment environment, and remarkably improves the adaptability to the environment change. The method has better real-time performance and is suitable for real-time application of an underwater unmanned platform.

The invention discloses an ophthalmology ultrasound RF signal enhancement method and device and a model training method and device, and relates to the technical field of ophthalmology ultrasound and the technical field of deep learning, and the method comprises the steps: obtaining to-be-enhanced ophthalmology ultrasound RF signal data of a target object, the to-be-enhanced RF signal data is obtained by performing ophthalmologic ultrasonic scanning on eyeballs of a target object through eyelids by adopting high-frequency pulse sound waves; rF signal data to be enhanced are input into a pre-trained signal enhancement model, blind deconvolution calculation is carried out on the RF signal data to be enhanced, obtained output serves as enhanced RF signal data, and the signal enhancement model is a deep learning model. By the adoption of the scheme, the comfort level of a patient during anterior segment ophthalmic ultrasonic examination is improved, and the examination efficiency is improved.

The present application relates to the technical field of image processing, and more particularly, to a kind of aluminized film quality detection method and system based on image recognition, comprising: obtaining the fuzzy image of aluminized film, and the aluminized film is printed with Chinese and English characters;The fuzzy image is divided into multiple image blocks, and for each image block, based on the gradient feature of the image block, its fuzzy intensity index is calculated.The present application divides the image into blocks, and calculates the fuzzy intensity index and the fuzzy sensitivity degree for each image block respectively, and then generates a spatial variability guide weight by combining the two.The weight can intelligently identify the key areas in the image that are both severely blurred and contain complex characters, and guide the blind deconvolution algorithm to focus on accurately restoring these areas, effectively solving the defect that traditional methods are difficult to handle spatially varying blur, and significantly improving the restoration quality of the blurred image.

The application relates to an OCT image processing method and system based on adaptive weighted bilateral filtering deconvolution, which comprises the following steps: preprocessing an original OCT signal, including removing background noise, wave number calibration, spectrum shaping and dispersion compensation, to obtain an OCT reconstructed image; adjusting the spatial weight and intensity weight of the OCT stretched image through histogram stretching to enhance contrast, and adaptively denoising the image; further optimizing the image by using a blind deconvolution algorithm, iteratively updating the estimated image and a point spread function (PSF), and obtaining a high-definition OCT reconstructed image. By using the adaptive weighted bilateral filtering and blind deconvolution technology, the application realizes efficient denoising and detail reservation of the OCT image, thereby obtaining a high-definition reconstructed image.

The invention discloses an unsupervised reconstruction method for adaptive optical image restoration, which is applied to the technical field of astronomical observation and aims to solve the problem that in the prior art, the imaging resolution is reduced due to atmospheric turbulence in the observation of a ground-based solar telescope. According to the method, multi-frame blind deconvolution is creatively fused into an unsupervised deep learning framework, an encoder-decoder network is constructed to generate a potential clear image, meanwhile, a depth priori condition network is constructed to optimize a fuzzy kernel, and efficient recovery is achieved by solving network parameters and the multi-frame blind deconvolution problem through alternate iteration. A GCM model is introduced into a corrector to ensure stable convergence, and a BM3D algorithm is adopted in the denoising process. Compared with the prior art, the method does not need a large amount of training data, is high in generalization capability, is high in recovery quality, can process non-isoplanatic and multiband images, achieves the complementary advantages of a conventional algorithm and deep learning, is high in calculation efficiency, is high in practicality, and provides a more advanced and reliable image processing technology for the fields of astronomical observation and the like.

A data receiving device with a blind deconvolution mechanism. A descrambling circuit descrambles the received data based on a single-antenna assumption and N data position assumptions within a transmission period, generating N sets of soft-bit data. A soft information processing circuit extracts bit position information to determine invariant and variable bit positions for a blind deconvolution process, including: cyclically storing and superimposing N sets of soft-bit data to generate N sets of superposition results, corresponding to N data position assumptions in N buffers of a buffer circuit; and retaining data corresponding to invariant bit positions at the start of a new transmission period. A post-processing circuit deinterleaves and decodes the N sets of superposition results corresponding to each data sub-block to generate N sets of decoded results for verification. When any N sets of decoded results pass verification, the soft information processing circuit terminates the blind deconvolution process.

The application discloses an on-board multi-sensor space-time online calibration method in a fully mechanized coal mining face complex environment and belongs to the technical field of image processing and environment perception. With the main IMU time axis as a unified reference, the time offset of the camera and the laser radar inertial measurement is estimated and compensated online, and a continuous time pose prediction model is established. The point cloud is subjected to motion compensation and time unification, combined with the time sequence motion and echo intensity consistency discrimination to remove the jitter ghost points, and the de-ghosting point cloud is obtained. Combined with the camera continuous time pose and the rolling shutter exposure model of each row, a spatially changing blur kernel is constructed, the non-blind deconvolution of the image is carried out to obtain a clear image. Finally, based on the de-ghosting point cloud and the clear image, a cross-modal structure consistency constraint is constructed, and the camera-laser radar space external participation time offset is solved through nonlinear optimization. The application can realize high-precision, lightweight and sustainable online space-time calibration, and improve the stability and reliability of the multi-modal data fusion of the on-board equipment in the fully mechanized coal mining face.

This invention discloses an automatic method for measuring the long axis of the aorta in left ventricular ultrasound images, relating to the field of medical image processing. The method involves collecting left ventricular ultrasound images, converting the original images into grayscale images, and processing each grayscale image using an improved semi-blind deconvolution algorithm. Key points are sequentially labeled with their serial numbers and coordinate positions on each processed image. A training set and a validation set are then divided from the labeled image set. An improved HRNet model is trained using the training set to obtain a key point detection model. Images from the validation set are input into the key point detection model, detecting six key points in each input image that correspond to the endpoints of the aortic long axis. Straight lines are drawn using each pair of key points as endpoints. The length of each straight line is calculated based on the coordinates of its endpoints and multiplied by the image scaling factor to obtain the aortic long axis measurement result.

The present disclosure discloses a data receiving apparatus and a data receiving method having blind deconvolution mechanism. A descrambling circuit descrambles received data according to an antenna assumption and N data position assumptions within a transmission period to generate N groups of soft-bit data. A soft-bit processing circuit retrieves bit position data to determine non-variable bit positions and variable bit positions. N circular buffers of a storage circuit store and superimpose the N groups of soft-bit data corresponding to N data position assumptions in a circular manner to generate N groups of superimposed results and keep the data corresponding to the non-variable bit positions. A post-processing circuit performs de-interleaving and decoding on the N groups of superimposed results to generate N groups of decoded results to perform redundancy check thereon. When one decoded results passes the redundancy check, the soft-bit processing circuit stops performing the blind deconvolution process.

The present application relates to a hyperspectral image sub-pixel positioning method with edge preservation, comprising: S1, using a Gaussian blur kernel and an original hyperspectral image to perform non-blind deconvolution to reduce the point spread function effect in the original hyperspectral image; S2, using a domain transform recursive filter to filter the result of step S1 to play a role in edge preservation and noise reduction; S3, obtaining an abundance image through a linear spectral unmixing technique; S4, using an interpolation method to up-sample the abundance image to obtain soft class values of each type of sub-pixel; S5, using a class assignment strategy to assign a class label to each sub-pixel to obtain the final sub-pixel positioning result. The present application can effectively process the point spread function effect and the noise such as texture details in the image, thereby improving the sub-pixel positioning accuracy.

The application relates to a multi-focus photoacoustic imaging registration fusion method based on blind deconvolution, which comprises the following steps: acquiring photoacoustic images of different laser wavelengths and different depths of field by using a fast photoacoustic imaging device; establishing a blind deconvolution model and initializing a blind deconvolution blur kernel; based on the blind deconvolution model, deconvolving the photoacoustic image and updating the blur kernel to obtain an optimized photoacoustic image through blind deconvolution, and continuously optimizing by using an image quality judgment function; using an intensity-based photoacoustic image registration algorithm to determine optimal transformation parameters based on intensity information, and registering the photoacoustic images of different depths of field after blind deconvolution optimization; using an image fusion algorithm to fuse the registered photoacoustic images to generate a focused fusion high-quality super-resolution photoacoustic image. Compared with the prior art, the application has the advantages of non-invasiveness, non-destructiveness, convenience, fast realization of photoacoustic image feature registration and fusion of different depths of field, and recovery of high-resolution imaging.

The present application relates to the technical field of unmanned aerial vehicle, especially to a kind of unmanned aerial vehicle aerial target detection repair method for optical distortion, comprising the following steps: obtaining unmanned aerial vehicle aerial video sequence, extract continuous frame image;Based on the statistical characteristics of thermal turbulence in a short time, construct space-time varying distortion model, distortion estimation is carried out to video sequence.The present application can effectively estimate and reverse optical distortion caused by thermal turbulence by constructing space-time varying distortion model and combining blind deconvolution technology, thereby significantly improving the definition and geometric consistency of unmanned aerial vehicle aerial image, this method makes full use of the time sequence information between continuous frames and turbulence statistical characteristics, realizes the accurate modeling of distortion field, and then restores the image with more detailed and clearer edges by inverse processing, provides high-quality input for subsequent target detection.

This invention discloses a mechanical fault diagnosis method based on maximizing the generalized cyclostationary index, belonging to the fields of signal processing and mechanical equipment condition monitoring and fault diagnosis. It mainly involves blind deconvolution of the signal based on maximizing the generalized cyclostationary index to extract the cyclostationary features of interest and perform envelope analysis. The method includes shape parameter estimation based on a generalized Gaussian cyclostationary model; blind deconvolution based on maximizing generalized cyclostationarity; and β-order envelope spectrum analysis. The beneficial effects of this invention are that, based on maximizing the generalized cyclostationary index, it improves the robustness of cyclostationary feature extraction for non-Gaussian distributed signals; and by setting a tolerance bandwidth in the weighting matrix generation, it solves the problem of inconsistency between the theoretical and actual cyclostationary frequencies.

The invention discloses a monitoring data processing method and system for a bee colony node of an unmanned aerial vehicle, and relates to the technical field of unmanned aerial vehicle data processing, and the method comprises the steps: collecting the junction temperature data and external flow field data of the unmanned aerial vehicle; calculating the Reynolds number based on the flow field data and the fuselage characteristic length, and judging the air flow state of the fuselage heat dissipation surface; in response to the fact that the junction temperature data exceeds a safety threshold value and the air flow state is laminar flow, generating a micro-maneuvering disturbance instruction, driving the unmanned aerial vehicle to generate micro-amplitude vehicle body vibration, and converting a vehicle body heat dissipation surface flow field into turbulent flow; and constructing a point spread function based on the waveform parameter of the micro maneuver control signal, carrying out non-blind deconvolution restoration on original monitoring data collected in a micro-amplitude machine body vibration state, and inputting the original monitoring data into a target detection network to carry out identification and analysis of a monitoring target. According to the method, the laminar flow boundary layer is damaged by micro-motor vibration, the heat dissipation efficiency is improved, non-blind deconvolution is performed by using the vibration parameters to eliminate imaging blur, and the continuous operation capability of the bee colony node under high load is ensured.

An image processing method for measuring object displacement is provided. The method includes acquiring a first sequence of images and a second sequence of images, two adjacent images of the first sequence of images including a first overlap portion, two adjacent images of the second sequence of images including a second overlap portion, the first sequence of images corresponding to a first three-dimensional (3D) surface including a curved surface on an object in a first state, the second sequence of images corresponding to a second 3D surface on the object in a second state. The method also includes deblurring the first sequence of images and the second sequence of images based on a blind deconvolution method to obtain sharp focal plane images, stitching the sharpened first sequence of images and the sharpened second sequence of images into a first 3D image and a second 3D image based on camera pose estimation performed by solving a perspective-n-point (PnP) problem using a refined robustly weighted Levenberg-Marquardt (RRWLM) algorithm. The method also includes forming a first two-dimensional (2D) image and a second 2D image by unwrapping the first 3D image and the second 3D image, respectively, and generating a displacement map image from the first 2D and second 2D images by performing a two-dimensional digital image correlation (DIC) method.

The invention relates to the technical field of image processing, in particular to an aluminum laminated film quality detection method and system based on image recognition, and the method comprises the steps: obtaining a fuzzy image of an aluminum laminated film printed with Chinese and English characters; and dividing the blurred image into a plurality of image blocks, and for each image block, based on the gradient feature of the image block, calculating to obtain a blurring intensity index of the image block. According to the method, the image is divided into blocks, the fuzzy intensity index and the fuzzy sensitivity degree are calculated for each image block, and then a spatial variability guide weight is generated by combining the fuzzy intensity index and the fuzzy sensitivity degree. The weight can intelligently identify key areas which are seriously blurred and contain complex characters in the image, and guide the blind deconvolution algorithm to focus on accurate restoration of the areas, so that the defect that a traditional method is difficult to process space change blurring is effectively overcome, and the restoration quality of the blurred image is remarkably improved.

A fault detection system is provided for extracting fault signatures from time-domain stator current signals of a motor under varying load operation by solving a joint blind deconvolution demodulation problem. The stator currents of the motor under varying operating conditions are modeled as stator currents under steady-state operating conditions affected by a system response vector and a load modulation vector. A proximal alternating linearization minimization method is used to solve the joint blind deconvolution demodulation problem, assuming that the spectrum of the desired signal is sparse. Motor fault detection is then performed using the recovered stator currents under steady-state operating conditions.

The invention discloses a computational adaptive optical microscopic imaging method based on sparse blind deconvolution. The method comprises the following steps: firstly, carrying out multi-frequency fusion filtering preprocessing on an acquired fluorescence image, inhibiting low-frequency noise and enhancing medium-high frequency details; then, based on image gradient sparse prior, the image and a point spread function are updated through blind deconvolution alternate iteration; then utilizing a Zernike polynomial to fit aberration to obtain an aberration-containing point spread function; and finally, performing deconvolution to realize image deblurring and aberration correction. According to the method, extra hardware and precise point spread function calibration are not needed, and high noise robustness and system universality are achieved; compared with traditional Richardson-Lucy deconvolution, the method has the advantages that the point spread function and the sample information can be reconstructed from a single blurred image at the same time, the requirement for accurate point spread function calibration is avoided, the image resolution and contrast of a wide-field and confocal fluorescence microscope system are improved, and the method is suitable for being applied to the fields of dynamic life science research, medical imaging and the like.

The invention discloses a bearing residual life prediction method based on elastic blind deconvolution and a time convolution neural network, and relates to the technical field of vibration signal intelligent fault diagnosis. The method comprises the following steps: collecting bearing acceleration signals; z-score normalization processing is carried out on the collected acceleration signals of the bearing; convolution layer parameter initialization is realized through uniform initialization; minimizing a nonlinear L1 / 2 norm through an Adam optimizer so as to enhance fault features in the acceleration signal; a step-by-step ablation strategy based on a harmonic energy distribution index is adopted, and irrelevant interference components and repeated fault components are eliminated in a fault feature enhancement process to improve the calculation efficiency and realize end-to-end fault feature output; monitoring an output signal by adopting a harmonic energy distribution index threshold value so as to determine the occurrence time of a bearing fault; and mapping the output signal into residual life by using a residual life prediction module.

A profile generation method and device based on an intelligent agent for high-resolution processing of seismic data, comprising processing original seismic data through a blind deconvolution model to obtain an initial wavelet and an initial reflection coefficient; convolving the initial wavelet and the initial reflection coefficient to obtain an initial seismic profile; extracting initial profile features of the initial seismic profile; inputting the initial profile features, parameters of the initial wavelet, and parameters of the initial reflection coefficient into a pre-trained reinforcement learning intelligent agent for calculation; updating the parameters of the initial wavelet and the parameters of the initial reflection coefficient by the reinforcement learning intelligent agent to obtain parameters of a final wavelet and parameters of a final reflection coefficient; and convolving the final wavelet and the final reflection coefficient to obtain a target high-resolution seismic profile. The method of the present application uses a convolution model as the physical basis of the reinforcement learning environment, realizes reinforcement learning driven by a physical model, and obtains a seismic profile with higher resolution and more consistent with actual geological structure.

The invention relates to the technical field of computer machine vision, in particular to a partially coherent X-ray laminated diffraction imaging reconstruction method and system based on nerve blind deconvolution, and the method comprises the steps: obtaining emergent waves of interaction between a lighting probe and a to-be-reconstructed sample at each scanning position of the to-be-reconstructed sample through a scanning probe; performing fast Fourier transform on the emergent wave to obtain a wavefront function formed by the emergent wave on a detector plane; enhancing the actually acquired diffraction pattern by using an unsupervised neural blind deconvolution network, and adjusting the wavefront function amplitude based on the enhanced diffraction pattern; and reversely propagating the adjusted wavefront in the free space to the plane of the to-be-reconstructed sample, updating the sample function and the probe function at the scanning position by using the new emergent wave until convergence, and obtaining the image output of the to-be-reconstructed sample. The method can flexibly adapt to enhancement requirements of different coherence conditions, does not need any priori knowledge, and does not need a completely coherent diffraction pattern as label data.

The application discloses a blind deconvolution method based on candidate fault frequency, and belongs to the field of rotating machinery state monitoring and fault diagnosis. Firstly, the measured signal is decomposed into different frequency bands to obtain a series of narrow-band signals with different center frequencies and bandwidths; then, based on the local features of the square envelope spectrum of the narrow-band signal, candidate fault frequencies possibly related to faults are identified, and then an optimal objective function is constructed by using the candidate fault frequencies; finally, an iterative solution strategy is adopted to solve the blind deconvolution filter, so that the bearing transient cyclic impact fault feature extraction is realized. Compared with existing deconvolution methods, the method fully excavates the time-frequency characteristics of the vibration signal, can effectively extract the local defect fault feature information of the rotating machinery, and is suitable for bearing fault diagnosis under the working condition of missing shaft rotating speed information.

The application discloses a kind of three-dimensional reconstruction method and system based on two-dimensional forward-looking sonar image, the method includes: step one: for the two-dimensional forward-looking sonar sequence collected, introduce based on mahalanobis distance detection method, determine the initial profile of local binary fitting level set segmentation method, further, using a local binary fitting (LBF) level set segmentation method, determine the target to be reconstructed region based on the initial profile detected;Step two: using the registration method based on phase correlation, the target region determined in step one is registered, and the relative displacement of sonar movement is calculated.Step three: based on the results of the above steps, using blind deconvolution algorithm, reconstructs the three-dimensional point cloud image of target.Compared with the prior art, the three-dimensional reconstruction of the target can be performed without pre-acquiring the horizontal position information of the sonar, and has certain anti-noise ability.The method disclosed in the application is more suitable for three-dimensional reconstruction of underwater targets.

This disclosure relates to a method, apparatus, electronic device, and storage medium for reconstructing a single-frame astronomical image, comprising: determining a blind deconvolution optimization model corresponding to an initial single-frame astronomical image using a blind deconvolution algorithm based on a reweighted graph total variational prior; resampling the initial single-frame astronomical image to determine n skeleton images corresponding to the initial single-frame astronomical image; performing iterative optimization and intensity correction processing based on the blind deconvolution optimization model and the n skeleton images to determine a target blur kernel corresponding to the initial single-frame astronomical image; and deblurring the initial single-frame astronomical image based on the target blur kernel to determine a deblurred single-frame astronomical image. This disclosure can utilize a blind deconvolution algorithm based on an RGTV prior to iteratively optimize and correct the intensity of the initial single-frame astronomical image, obtaining a high-quality deblurred single-frame astronomical image with less computational resources and time required, thereby reducing image reconstruction costs and improving processing efficiency.

The invention discloses a self-adaptive three-dimensional deconvolution method for a fluorescence microscope with physical fidelity, and belongs to the field of fluorescence microscopic imaging. Noise characteristic self-adaptive analysis is carried out according to a collected three-dimensional body image, a three-dimensional point spread function is reconstructed according to spread spectrum of average intensity projection of the three-dimensional body image, iterative deconvolution operation is carried out on the three-dimensional body image according to a noise characteristic coefficient and the three-dimensional point spread function, and the three-dimensional body image after deconvolution is obtained. Efficient three-dimensional adaptive deconvolution is realized, and the noise robustness is improved; according to the method, high complexity of theoretical reconstruction and mismatch with actual conditions are avoided; meanwhile, the construction process of the three-dimensional point spread function is derived from system characteristics reflected by a three-dimensional body image, and compared with a blind deconvolution method, the method has the advantage of physical fidelity. The method is used for improving the signal-to-noise ratio and the resolution ratio of mainstream three-dimensional fluorescence microscopic systems such as wide-field, spot scanning confocal, turntable confocal and structured light illumination microscopes.