53 results about "Blind deconvolution" patented technology

The invention relates to the technical field of space telescope image processing, and particularly provides a large-view-field spliced space telescope image high-precision restoration method, which comprises the following steps of: constructing a distribution relation of wavefront aberrations with different frequencies along with a view field according to optical parameters of a telescope; based on the relation between the intensity two-dimensional matrix of any field-of-view image plane PSF image and the wave aberration of the corresponding field-of-view, an image plane PSF image of a specific field-of-view is actually measured through a wavefront sensor arranged on the telescope, and the wave aberration of the specific field-of-view is calculated based on the actually measured image plane PSF image; the PSF of any view field is derived according to the distribution rule of wavefront aberrations with different frequencies along with the view field and the relation between the intensity two-dimensional matrix of the image plane PSF image and the wave aberration, and the PSF of any view field derived from the actually measured PSF is utilized to realize the high-precision restoration of the spliced space telescope image through an image block restoration method. According to the method, the actually-measured PSF is adopted for image restoration, the defect that estimation PSF is adopted during traditional non-blind deconvolution is overcome, and the restoration precision is improved.

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 invention provides an electromagnetic interference elimination method, apparatus and device for a video monitoring image. The method comprises the steps of obtaining a to-be-processed video monitoring image; performing blind deconvolution processing on the to-be-processed video monitoring image to obtain an airspace de-noised image; discrete wavelet transform is carried out on the spatial domain denoised image to obtain a frequency spectrum image of the spatial domain denoised image; filtering electromagnetic interference noise in the frequency spectrum image of the spatial domain denoised image based on a preset denoising threshold to obtain a frequency domain denoised image; and carrying out two-dimensional discrete wavelet inverse transformation on the frequency domain denoised image to obtain a video monitoring image after electromagnetic interference is removed. According to the method, a blind deconvolution method is used in a spatial domain, a threshold denoising method based on wavelet transform is used in a frequency domain, and compared with a traditional single spatial domain denoising or frequency domain denoising method, electromagnetic interference can be effectively filtered out, original high-frequency components of a video are guaranteed, high-frequency detail content is reserved, and the method is suitable for large-scale popularization and application. And the restored image distortion caused by image detail loss is avoided.

The invention relates to the technical field of blurring detection, and discloses a motion blurring detection method and system based on deep learning and blind deconvolution, and the method comprises the steps: collecting an original image, and carrying out the sub-region division of the image; singular value decomposition is carried out on each sub-region; performing fuzzy region detection on the sub-regions according to a decomposition result; performing image restoration on the blurred image; and segmenting target contour information in the fuzzy restored image by using a deep learning method, and outputting a clear image with a target contour. By introducing the SPD-Conv, the improved YOLOv8 model can more accurately segment a low-resolution fuzzy target, and the segmentation precision is effectively improved. And the method can adapt to low-resolution fuzzy target segmentation tasks in different scenes. While the segmentation precision is improved, the calculation complexity and the reasoning time of the model are not remarkably increased, the real-time advantage of the YOLOv8 can still be kept, and the requirement for real-time performance in practical application is met.

The invention discloses a railway switch machine fault analysis method and system. The method comprises the steps that three-phase action current is converted into three-phase action current digital signals according to a preset digital conversion strategy; cyclic frequency information in the three-phase action current digital signal is extracted, and an enhanced envelope spectrum is generated; quantifying and accumulating relative characteristics of a plurality of cyclic frequencies in the enhanced envelope spectrum to obtain a diagnosis characteristic spectrum, and identifying an equal-frequency interval harmonic structure in the enhanced envelope spectrum according to the diagnosis characteristic spectrum to obtain cyclic frequencies; and feeding the cyclic frequency to a preset maximum cyclostationary blind deconvolution, outputting the maximum cyclostationary blind deconvolution to obtain a repeated transient pulse caused by the local defect, and determining a fault signal of the railway switch machine according to the repeated transient pulse. The accuracy of railway point switch fault recognition is improved, fault related signals can be well recognized under the condition that strong noise interference exists outside, and troubleshooting of railway point switch faults is facilitated.

The invention relates to an adaptive weighted bilateral filtering deconvolution OCT image processing method and system, and the method comprises the steps: carrying out the preprocessing of an original OCT signal, including background noise removal, wave number calibration, spectrum shaping and dispersion compensation, so as to obtain an OCT reconstructed image; enhancing the contrast ratio through histogram stretching, adjusting the space weight and the intensity weight of the OCT stretching image, and performing adaptive denoising on the image; and further optimizing the image by using a blind deconvolution algorithm, and iteratively updating the estimated image and a point spread function (PSF) to obtain a high-definition OCT reconstructed image. According to the method, efficient denoising and detail reservation of the OCT image are realized through adaptive weighted bilateral filtering and blind deconvolution technologies, so that a high-definition reconstructed image is obtained.

The invention discloses a fault diagnosis method and device, a product, equipment and a medium. The method comprises the following steps: acquiring a fault signal of a target wind power gear box; determining an optimal parameter of signal resonance sparse decomposition based on a first swarm intelligence optimization algorithm, and obtaining a target high resonance component and a target low resonance component obtained by performing signal resonance sparse decomposition on the target wind power gear box fault signal based on the optimal parameter of signal resonance sparse decomposition; determining an optimal parameter of blind deconvolution based on a second swarm intelligence optimization algorithm, and obtaining the optimal parameter based on the blind deconvolution to perform blind deconvolution processing on the target high-resonance component and the target low-resonance component to obtain a high-resonance component after target blind deconvolution processing and a low-resonance component after target blind deconvolution processing; and performing envelope demodulation on the high-resonance component after target blind deconvolution processing and the low-resonance component after target blind deconvolution processing so as to determine the fault type of the wind power gear box. Therefore, the fault diagnosis accuracy of the wind power gear box can be improved.

The invention belongs to the technical field of fan bearings, and particularly discloses a fan bearing fault identification method and system.The method comprises the steps that vibration acceleration sensors are arranged at a fan installation base and a motor rear end cover, fan vibration acceleration signals are obtained and filtered, and the cycle frequency alpha of the obtained fan vibration acceleration signals is estimated; finding an optimal filter coefficient; if the convergence condition is met, the kurtosis of the output signal xi is calculated, the optimal filter is obtained by combining the optimal filter coefficient and the optimal filter length, the fan vibration acceleration signal is input into the optimal filter, the filtered deconvolution signal is output, and the envelope amplitude spectrum of the deconvolution signal is obtained based on Hilbert transform demodulation; and comparing with a preset bearing fault characteristic frequency, and diagnosing the fault type of the fan bearing. By adopting the technical scheme, an accurate fan bearing fault diagnosis result is obtained based on improved self-adaptive maximum second-order cyclostationary blind deconvolution.

The present invention relates to a blind deconvolution restoration method and system for DR blurred images, the method steps comprising: based on the grayscale distribution of DR images being concentrated and continuously constrained by using the minimum value of the image region, combining the L2 norm to characterize the sparse characteristics of the gradient, suppressing image noise by total variation, establishing a blind deconvolution mathematical model under the maximum a posteriori probability framework, solving the potential clear image and blur kernel by using the semi-quadratic splitting rule, and finally restoring the clear image by combining the R-L deconvolution method. The blind deconvolution method proposed by the present invention for the characteristics of DR blurred images can effectively remove the blur of DR images, restore the key information of the image, and achieve stable and high-quality restoration of DR blurred images. Compared with the prior art, the present invention can effectively remove the blur of DR images, restore the key information of the image, and achieve stable and high-quality restoration of DR blurred images.

The invention discloses a fan blade aerial image deblurring method fused with auxiliary image prior, which comprises the following steps of: establishing an image degradation model, estimating a blurring kernel and a potential image in the image degradation model by optimizing a target function, and after the blurring kernel is obtained, performing image deblurring on the image degradation model; non-blind deconvolution is applied to the input blurred image to generate a final clear image. According to the method, the auxiliary image is introduced in the process of estimating the blurring kernel, the structural consistency and defect identification degree of the recovered image are remarkably improved, a breakthrough from natural image priori to task specific priori is achieved, after the blurring kernel estimation is completed, standard deconvolution is not directly used, and the algorithm is simple and convenient to operate. Compared with the prior art, the non-blind deconvolution energy model fusing multiple image structure priori is innovatively proposed, the common problems of artifacts, edge blurring and the like in a traditional deconvolution image are greatly solved, high-fidelity image reconstruction is achieved, and the accuracy and robustness of a subsequent defect detection system are enhanced.

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 present invention provides a natural image deblurring method based on structure extraction, which estimates the blur kernel after denoising preprocessing the image, and then performs a non-blind deconvolution step on the image to obtain the deblurred image; the method comprises the following steps: step S1, denoising preprocessing the original blurred image P1 to obtain P2; step S2, estimating the intermediate latent image of the preprocessed image P2 to obtain the image P3; step S3, extracting the main structure of the intermediate latent image P3, and obtaining the image P4 after some adjustments; step S4, using the image P4 for kernel estimation; step S5, performing non-blind deconvolution to obtain the deblurred image P5. The application of this technical solution can avoid the adverse effects of noise on the subsequent kernel estimation step. The continuity of the blur kernel can be guaranteed while ensuring the sparsity of the blur kernel.

The invention provides a blind deconvolution model, a training method, a blind deconvolution method, equipment and a medium. The blind deconvolution model comprises a neural network used for predicting a sparse coefficient; and the convolutional network is used for performing one-dimensional convolution on the sparse coefficient predicted by the neural network and the convolution kernel representing the seismic wavelet to obtain synthetic seismic data. According to the invention, a closed-loop deep learning network framework and a blind deconvolution thought are utilized to build an intelligent blind deconvolution network model. A step-by-step alternating strategy is used for weight updating of a U-Net network model and a one-dimensional convolution kernel representing seismic wavelets, inversion of a sparse coefficient and the seismic wavelets is achieved at the same time, the requirement for seismic wavelet extraction precision in a traditional method is avoided, and manual intervention is reduced. Physical equation constraints reduce multiplicity of solutions, improve network stability, ensure processing precision, can be used for surface consistency deconvolution and high-resolution processing, and provide more reliable data for seismic high-fidelity imaging and interpretation.

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 present invention discloses a mechanical fault diagnosis method and system based on blind deconvolution. The method comprises: picking up a mechanical vibration observation signal through an acceleration sensor; constructing a weighted energy threshold coefficient, and performing single source point detection on the mechanical vibration observation signal collected by the sensor based on the weighted energy threshold coefficient to obtain feature data and an estimated number of sources H; normalizing the filtered feature data to obtain feature data represented by directional angles; clustering the feature data represented by directional angles to obtain cluster centers and membership degrees; reconstructing and separating H source signals containing single faults from a signal containing composite faults based on the cluster centers and membership degrees, performing an inverse short-time Fourier transform on the separated source signals to obtain time-domain source signals; performing feature enhancement on the time-domain source signals; and performing envelope analysis on the enhanced signals to determine the fault type. The present invention can achieve blind separation of composite faults when the number of sources is unknown, and use mechanical vibration signals for fault feature extraction and diagnosis.

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 invention provides a computational adaptive optical microscopic imaging method based on sparse blind deconvolution. According to the method, sparse prior of a fluorescence sample and an aberration correction algorithm based on a Zernike polynomial are combined, compared with traditional Richardson-Lucy deconvolution, a point spread function and sample information can be reconstructed from a single blurred image at the same time, the requirement for accurate point spread function calibration is avoided, and the method is suitable for large-scale popularization and application. And the method is better in the aspects of improving the robustness to noise and stabilizing the deconvolution process. According to the invention, the image resolution and contrast of the wide-field and confocal fluorescence microscope system are obviously improved, and the wide-field and confocal fluorescence microscope system is suitable for urgent demands in the fields of dynamic life science research, medical imaging and the like, and has a wide application prospect.

The invention discloses a star sensor image restoration method based on blind deconvolution, and relates to the technical field of star sensor image processing. Comprising the following steps: acquiring a real-time star map through an imaging system, reading a whole star-sensitive image of the real-time star map, estimating the positions of star points according to the whole star-sensitive image, windowing on the whole star-sensitive image by taking each star point as a center to obtain a sub-image, and restoring the sub-image into a target image. And calculating the horizontal and vertical coordinate positions of the mass center of the star point according to the recovered target image, and outputting the position of the star point to the star map recognition module, thereby solving the problem of inaccurate judgment of the position of the star point under aerodynamic turbulence, and improving the mass center extraction precision of the target.

The present invention discloses an unsupervised domain adaptive image non-blind deconvolution method, device and medium, wherein the method comprises: determining source domain data and target domain data; constructing a non-blind deconvolution model; using the source domain data to perform supervised training on the non-blind deconvolution model to obtain a pre-trained model; applying the obtained pre-trained model to the target domain data, and using unsupervised adaptive loss to train the pre-trained model to obtain a migration model; judging whether the deconvolution accuracy of the migration model meets the preset conditions, if so, stopping the training, otherwise continuing the training; applying the migration model after the migration training to the target domain data, and outputting the deconvolution result of the test image. The present invention migrates the pre-trained model without accessing the clear images in the source domain data and the target domain data, thereby improving the non-blind deconvolution performance of the model in the target domain. The present invention can be widely used in the field of image non-blind deconvolution.

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.

The invention specifically discloses a fault impact deconvolution tracking method for adaptive period detection of a rail transit intelligent bearing, and the method comprises the following steps: obtaining a vibration acceleration signal of the intelligent bearing, setting an expected filter length range, dividing the filter length range into two subintervals, determining three special points, searching an optimal filter coefficient; and calculating and comparing target harmonic energy ratios THER in the blind deconvolution signal envelope spectrums of the three special points, determining the position of the maximum value, determining a filter length search interval, if the interval length reaches a threshold condition, outputting the filter length and the optimal filter coefficient corresponding to the maximum index, and outputting the final filtered blind deconvolution signal. And obtaining an envelope spectrum of the blind deconvolution signal, and evaluating the degradation state of the intelligent bearing. By adopting the technical scheme, vibration signal acquisition, cyclic frequency estimation, recursive search and fault diagnosis are performed, the fault period of the intelligent bearing is positioned, and the calculation efficiency is remarkably improved while the diagnosis precision is ensured.

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.