58 results about "Homomorphic filtering" patented technology

The invention discloses an image processing method applied to printed matter surface color difference detection, which relates to the technical field of image processing, and comprises the following steps: acquiring a digital image of a printed matter to be detected, performing illumination non-uniformity correction, and converting the corrected digital image into a CIELAB color space; performing region segmentation on the digital image based on double constraint conditions of color gradient and texture boundary to generate a detection region graph; feature parameters are extracted based on the detection area graph, and a feature data set is generated; establishing a dynamic reference model by utilizing process parameters and material characteristics of the printed matter; and calculating the distance between the feature data set and the dynamic reference model, identifying color difference regions and generating color difference scores, and screening and grading the color difference regions according to the color difference scores. According to the method, a multi-layer image pyramid and homomorphic filtering combined illumination correction technology is adopted, and an adaptive weight fusion mechanism based on image features is introduced, so that the illumination nonuniformity is effectively eliminated while the definition of printing details is kept.

The invention relates to the field of intelligent detection, and provides an LCD system fault detection system and method based on image analysis, and the method comprises the steps: firstly obtaining an LCD panel image collected by a high-resolution industrial camera, then carrying out the homomorphic filtering and CLAHE contrast enhancement processing of the image, and obtaining an enhanced LCD panel image; the method comprises the steps that firstly, an LCD panel image is obtained, then multi-scale feature enhancement fusion is carried out on the enhanced image to obtain an LCD panel image multi-scale fusion feature map, then statistical analysis is carried out on the feature map based on a Mura segmentation area to obtain a Mura area statistical feature coding vector, and finally a Mura defect recognition result is obtained according to the coding vector. Therefore, the characteristics of the Mura defect can be captured more accurately, misjudgment and missing detection caused by interference factors such as a light source and noise are reduced, the detection precision of the Mura defect is improved, and meanwhile, the detection system can adapt to different production conditions and ensure the stability of a detection result.

The invention discloses an X-ray detection image enhancement method and system, and the method comprises the following image enhancement steps: carrying out the gray preprocessing of an image, carrying out image enhancement preprocessing, carrying out homomorphic filtering enhancement processing, carrying out USM sharpening processing, taking a filtered image Ifiltering as an input, carrying out the processing of an input image through Gaussian blur, generating an enhanced image, and finally carrying out the tone adjustment. And carrying out color gradation adjustment on the enhanced image Ishadow to obtain a final target image Itarget. According to the method, firstly, multiple parameters such as exposure, brightness and contrast are adjusted for the original image, so that the overall quality of the image can be improved more comprehensively. Compared with the prior art, the multi-dimensional adjustment provides greater flexibility, so that the image can reach the optimal basic state before entering the subsequent processing. And a foundation can be laid for subsequent image post-processing, image analysis, image classification and image library and map establishment.

The invention belongs to the technical field of underwater small target object analysis and recognition methods, and particularly relates to a sound signal feature optimization method for underwater small target object analysis and recognition. Comprising the following steps: 1, acquiring and processing original data; 2, eliminating interference line spectrums, and intercepting and splicing by adopting a time window to obtain a time recombination sound signal; using range transformation to optimize signal amplitude characteristics; processing the sound signal by using an FIR digital high-pass filter; obtaining an optimized signal by using a homomorphic filter; 3, extracting structural features, energy features, frequency features and sound intensity features of the optimized digital signals; step 4, sound signal feature fusion: carrying out underwater target sound signal feature fusion processing based on a canonical correlation analysis method; the invention provides a method for extracting effective sound signal data of an underwater target under the background of optimizing a complex marine environment, enhancing signal effectiveness during long-distance detection, and constructing effective sound signal features capable of being used for classification and recognition of the underwater target at the same time.

The invention discloses a process casting image surface defect automatic identification method based on machine vision, which comprises the following steps: acquiring an original image of a process casting, recording an imaging parameter set, and preprocessing to obtain a standardized image; performing homomorphic filtering to obtain an enhanced image and an illumination component, and inputting the enhanced image into an improved OfficientAD algorithm to generate an abnormal score map and a feature residual map; updating homomorphic filtering parameters according to the abnormal score map, the feature residual map and the illumination component, and performing homomorphic filtering for three times to obtain an enhanced image; inputting the enhanced image into an improved OfficientAD algorithm to obtain an abnormal score graph; and performing threshold segmentation and connected domain extraction on the abnormal score graph to obtain a defect candidate region, extracting texture statistics and residual statistics, and matching a defect type feature library to output an identification result. According to the method, stable and consistent automatic identification is realized under complex texture and imaging conditions, and the manual visual inspection cost is reduced.

The invention relates to the technical field of industrial vision, in particular to an automobile part surface defect detection method and system based on machine vision, and the method comprises the steps: firstly loading a CAD three-dimensional model, and rendering an ideal fringe reflection map in a virtual environment; calculating an optical distortion correction matrix by comparing the phase distortion with the phase distortion of an actual initial reflection image, driving a programmable light source to project a compensation pattern, and generating a normalized reflection intensity image; signal object decoupling is achieved through multi-scale wavelet transform, and high-frequency components and low-frequency components are separated; homomorphic filtering is applied to the low-frequency component to construct a homogenized background model, the high-frequency component is reversely corrected, and a high-signal-to-noise-ratio defect signal image is output; generating a coarse segmentation mask by the high-frequency signal through an adaptive local threshold, generating a morphology anomaly mask by the low-frequency signal through Hessian matrix morphological analysis, and fusing to form a collaborative segmentation mask; and extracting multi-dimensional geometric attributes of connected domains in the masks, and inputting the multi-dimensional geometric attributes into a decision tree to realize accurate classification and confidence output of defects.

The invention discloses a distribution network hot-line work robot pose estimation system and method, and belongs to the technical field of robot visual perception. The system comprises an input preprocessing module which is used for carrying out noise reduction and enhancement processing on an RGB-D image; the shared feature extraction module is used for extracting multi-scale universal features based on a lightweight convolution architecture; the 6D pose estimation module is used for processing the image based on the neural implicit field to obtain a pose estimation result; and the joint optimization module is used for realizing detection and pose estimation shared feature extraction through cooperation of a multi-task loss function and a pose estimation result. The system adopts adaptive median filtering and homomorphic filtering to eliminate noise and uneven illumination, and bilateral filtering optimizes a depth map; a bottleneck structure and cavity convolution are introduced into feature extraction, and a rank enhancement linear attention module is embedded; according to the pose estimation, a geometric field and an appearance field are modeled through a neural implicit field, and pose hypotheses are generated and optimized. According to the method, the problems of low pose estimation precision and poor real-time performance in the distribution network live working environment are solved, and the working safety and efficiency of the robot are improved.

The invention discloses a borehole optical image crack intelligent quantification method for diaphragm wall quality evaluation, belongs to the technical field of diaphragm wall quality, and aims to solve the problems of difficulty in micro crack identification, high subjectivity of geometric feature quantitative judgment and low detection efficiency in manual detection. Comprising the following steps: acquiring a high-definition image of a borehole wall, eliminating mirror reflection through adaptive homomorphic filtering, and generating an illumination invariance image; inputting a U-Net + + network (a crack segmentation model) fused with a histogram of oriented gradient enhancement module, and identifying a result containing width; a crack pixel of 0.1 mm; the length, the bifurcation angle and the tendency are calculated through fracture topology connectivity analysis; predicting a potential penetration path in combination with a soil stress field model; calculating an index representing the integrity of the diaphragm wall according to the segmentation result, the geometric features and the potential path; and generating a crack risk assessment report based on the index.

The invention belongs to the field of robot technology and computer vision, and particularly relates to an instant positioning and mapping method based on multi-source sensor information efficient fusion optimization. The method comprises the following steps of: firstly, adding effective gradient change characteristics for a weak texture environment by actively projecting infrared speckle textures; improving the contrast ratio of the weak texture region image by combining a homomorphic filtering method; then, utilizing an AGAST adaptive feature extraction method to enhance the feature detection sensitivity and stability in a low-light environment; high-frequency motion information provided by an IMU sensor is fused with environment visual features obtained by a camera, a comprehensive nonlinear optimization model is constructed, system state errors are accurately modeled, noise and uncertainty influences are effectively reduced, and optimal solution of the system state is achieved. And finally, on the basis of the probability grid sub-graph and the probability model, effective depth information is obtained by combining internal reference of the camera and a visual field range, and construction, directional expansion and dynamic updating of a global grid map are finally realized.

The invention discloses an object recognition method and device, a storage medium and electronic equipment. The method comprises the steps of obtaining a to-be-recognized target image; processing the target image through a denoising parameter prediction network to obtain a denoising parameter vector predicted for the target image, the denoising parameter vector comprises a filtering parameter used for carrying out homomorphic filtering processing on illumination information of the target image, a scale transformation parameter used for carrying out multi-scale transformation denoising processing on the target image, and an operation parameter used for carrying out image enhancement processing on the target image; performing de-noising processing on the target image according to the de-noising parameter vector to obtain a de-noised target image; and identifying a target object from the denoised target image through a target detection network. The technical problem that the recognition accuracy of the target object in the image is low due to the fact that the quality of the image collected by the image collection equipment in severe weather is poor is solved.

The invention relates to the technical field of remote sensing image processing, in particular to a homomorphic filtering-CLAHE remote sensing image enhancement method based on integration strategy multi-target particle swarm optimization, which comprises the following steps: acquiring remote sensing image data, and preprocessing the remote sensing image data; converting the preprocessed remote sensing image into an HSV color space, and extracting a V component in the HSV color space; constructing a frequency domain-spatial domain hybrid enhancement framework of homomorphic filtering and contrast-limited adaptive histogram equalization, and performing enhancement processing on a V-channel component by using the framework; constructing an integrated strategy multi-target particle swarm optimization algorithm; constructing a four-target fitness function including structural similarity, average gradient, information entropy and gray variance, and guiding particles to search in the direction of optimizing a plurality of key image quality indexes at the same time by using the function so as to realize optimal selection of remote sensing image enhancement parameters; the method can effectively enhance the definition and structural integrity of the terrain texture in the remote sensing image of the complex mountainous area.

The invention discloses a non-woven fabric quality detection method, and belongs to the technical field of quality detection, and the method comprises the following steps: S1, collecting a production image of a non-woven fabric, and carrying out the preprocessing of the production image through a homomorphic filtering method, so as to obtain a stable production image; s2, extracting characteristic elements of other pixel points except the first row and the last row in the stable production image by using a gray-level co-occurrence matrix, and generating texture characteristics; and S3, taking the pixel points of which the gray values are smaller than the texture characteristics as quality defect positions of the non-woven fabric. According to the invention, flaws and defects on the surface of the non-woven fabric can be accurately identified; labor cost can be saved for enterprises, and the quality of the non-woven fabric is strictly controlled.

The invention provides a metal additive mold defect detection method and system based on a wavelet function, and relates to the field of graph attribute analysis. The method comprises the following steps: firstly, acquiring a multi-dimensional surface image of the metal additive mold from an MES (Manufacturing Execution System) through a network interface; secondly, a homomorphic filtering algorithm is adopted to carry out normalization processing on the metal additive mold image with non-uniform illumination, and non-uniform illumination correction and texture enhancement are achieved; thirdly, performing three-layer decomposition on the processed image by using a DB4 wavelet function, and calculating an energy ratio of each frequency band; then, by constructing a self-adaptive threshold model and morphological operation, metal additive mold defect analysis is carried out based on the abnormal metal surface image features, automatic detection of the metal additive mold is achieved, and a detection report is generated; and finally, calling an REST interface to automatically report a metal additive manufacturing defect detection result to an MES system.

The present application provides a kind of railway tunnel construction leakage risk identification disposal method and device, and the image to be analyzed is reconstructed after being transmitted by being stored after data block, block compression, relationship mapping and coding by preset link;Enhancement and denoising processing are carried out on the image using median filtering and homomorphic filtering;A railway tunnel construction seepage risk identification model based on multiple encoders and decoders is constructed to identify the location and risk level of seepage, the encoder uses multi-layer convolution and hollow pyramid pooling operation to extract image features, the output of the corresponding stage encoder is input into the decoder through jump connection, and the jump connection integrates a self-attention mechanism module.The present application can efficiently and accurately identify the location and risk level of railway tunnel construction seepage, effectively improve the monitoring efficiency and accuracy, realize the overall coverage of the whole operation surface, and is not affected by the construction environment, which provides a strong guarantee for the safety of railway tunnel construction.

The invention discloses a diffraction image quality improving method based on intrinsic stray light correction, and belongs to the technical field of diffraction image processing. The method comprises the following steps: firstly, establishing a calibration sample library by shooting a multi-scene image so as to determine a frequency spectrum parameter and a weight parameter of intrinsic stray light of a camera; secondly, logarithmic transformation is carried out on the diffraction image, multiplicative noise is converted into additive noise, after Fourier transformation is carried out on the additive noise to a frequency domain, stray light stripping is carried out through a Gaussian homomorphic filter, and then inverse transformation is carried out to recover the image; and finally, carrying out low-high frequency information superposition on the corrected image based on a non-mask sharpening algorithm so as to further improve the definition. According to the method, intrinsic stray light in the diffraction image is effectively eliminated, the image contrast and detail performance are remarkably improved, and an effective back-end processing scheme is provided for practical application of the diffraction imaging technology.

The invention belongs to the technical field of image processing, and provides a low-illumination image enhancement method based on an HSV (hue, saturation and value) color space, which comprises homomorphic filtering preprocessing, HSV space mapping, pixel truncation linear stretching, gamma correction processing, contrast enhancement processing, saturation optimization, RGB (red, green and blue) space mapping and color balance processing. Through fusion of pixel truncation linear stretching, gamma correction processing and contrast enhancement processing, parts with extreme pixel value distribution are eliminated, the pixel values of the image in a target range are enhanced, and the brightness and contrast of the low-illumination image are improved; through saturation optimization, the naturalness of the image is kept while the color brightness is enhanced, and color distortion is avoided.

The present application provides a multimodal medical image fusion method, device, equipment and computer-readable storage medium based on homomorphic filtering. The multimodal medical image fusion method based on homomorphic filtering includes: performing wavelet transform on CT images and MRI images to obtain approximate coefficients and wavelet coefficients of the CT images and MRI images respectively; performing pixel value comparison based on the approximate coefficients of the CT images and MRI images to determine the weight map; performing homomorphic filtering on the weight map to obtain a precise weight map; performing weighted fusion based on the approximate coefficients and precise weight map of the CT images and MRI images to obtain fused approximate coefficients; performing weighted fusion based on the wavelet coefficients and precise weight map of the CT images and MRI images to obtain fused wavelet coefficients; performing inverse wavelet transform based on the fused approximate coefficients and fused wavelet coefficients to obtain a fused image of the CT image and the MRI image. According to the embodiment of the present application, the problem of fuzzy fused images and image artifacts can be solved.

U I T T R E K S E L Disclosed is a remote sensing image enhancement method and system based on homomorphic filtering topographic correction. The method includes: after acquiring a remote sensing image to be enhanced, converting the image from a red—green—blue (RGB) color space to a hue—saturation—value (HSV) color space, and extracting a brightness component; performing homomorphic filtering processing on the brightness component; combining’ the processed. brightness component with saturation and. hue, and. inverting the component back to the RGB color space to complete image enhancement; acquiring grayscale distribution of the enhanced image, and calculating image information entropy; further calculating contrast of the image through grayscale values of pixels; calculating an enhancement score of the image based on the information entropy' and. contrast, and. comparing' the enhancement score with a preset score to determine whether the image enhancement is completed. The present invention can effectively improve the image quality of remote sensing images. (+ Fig. 1)

The application discloses a camera calibration method and system under uneven illumination conditions, and belongs to the technical field of image processing, and comprises the following steps: S1, processing an input image by using a multi-scale Retinex algorithm; S2, performing Gaussian homomorphic filtering processing on the image obtained in the step S1; S3, processing the image obtained in the step S2 by using an Otsu algorithm; and S4, performing camera calibration on the image processed in the steps S1-S3 by using Zhang Zhengyou plane calibration method. The application firstly enhances image details by using the multi-scale Retinex algorithm and the Gaussian homomorphic filtering, then obtains a final calibration board image by combining Otsu algorithm image segmentation and bilateral filtering processing, and further realizes high-precision calibration of the camera, and is worth popularization and use.

The present invention discloses a non-woven fabric quality inspection method, which belongs to the field of quality inspection technology, and includes the following steps: S1, collecting the production image of the non-woven fabric, and using the homomorphic filtering method to complete preprocessing of the production image to obtain a stable production image; S2, using the grayscale co-occurrence matrix to extract the characteristic primitives of the remaining pixel points in the stable production image except the first row and the last row, and generate texture characteristics; S3, taking the pixel points whose grayscale value is less than the texture characteristic as the quality defect position of the non-woven fabric. The present invention can accurately identify the flaws and defects on the surface of the non-woven fabric; it can save labor costs for enterprises and strictly control the quality of non-woven fabrics.

The invention relates to the technical field of image filtering, and provides a cable X-ray image homomorphic filtering enhancement method comprising the following steps: collecting a cable X-ray image, setting an initial value of a sharpening coefficient, and obtaining a cable X-ray enhanced image; obtaining a significance cluster and a length cluster, and calculating the gray level variation degree, the image richness variation degree and the sharpening coefficient suppression degree of the cable X-ray enhanced image; and according to the gray level change degree, the image richness change degree and the sharpening coefficient suppression degree, determining the value of a sharpening coefficient of homomorphic filtering, performing image enhancement on the cable X-ray image by using homomorphic filtering, and obtaining an image enhancement result of the homomorphic filtering. According to the invention, the processing effect of homomorphic filtering image enhancement can be improved.

The application provides a metal additive mold defect detection method and system based on a wavelet function, and relates to the field of graphic attribute analysis. The method comprises the following steps: firstly, acquiring a multi-dimensional surface image of a metal additive mold from a MES system through a network interface; secondly, performing normalization processing on a metal additive mold image with uneven illumination by adopting a homomorphic filtering algorithm, so as to realize non-uniform illumination correction and texture enhancement; thirdly, performing 3-layer decomposition on the processed image by using a DB4 wavelet function, and calculating the energy proportion of each frequency band; fourthly, carrying out metal additive mold defect analysis based on abnormal metal surface image features by constructing an adaptive threshold model and morphological operation, so as to realize automatic detection of the metal additive mold and generate a detection report; and finally, calling a REST interface to automatically report the metal additive manufacturing defect detection result to the MES system.

The invention provides a railway tunnel construction water leakage risk identification and disposal method and device. The method comprises the following steps: acquiring a to-be-analyzed image which is transmitted and reconstructed after data blocking, in-block compression, relation mapping and code storage through a preset link; carrying out enhancement and denoising processing on the image by adopting median filtering and homomorphic filtering; a railway tunnel construction water seepage risk identification model based on a plurality of encoders and decoders is constructed to identify a water seepage position and a risk level, the encoders extract image features by adopting multilayer convolution and cavity pyramid pooling operation, the input of the decoders is in jump connection with the output of the encoder of the corresponding level, and a self-attention mechanism module is integrated at the jump connection position. According to the method, the water seepage position and the risk level of railway tunnel construction can be efficiently and accurately identified, the monitoring efficiency and accuracy are effectively improved, the overall coverage of the whole working face is realized, the method is not interfered by the construction environment, and a powerful guarantee is provided for the safety of railway tunnel construction.

The invention relates to a photovoltaic module defect high-quality segmentation method based on fuzzy hint and a large model, and the method specifically comprises the following steps: separating a high-frequency part and a low-frequency part of an electroluminescent image through Fourier transform based on a homomorphic filtering method, carrying out the independent processing of the high-frequency part and the low-frequency part, carrying out the clustering of the enhanced EL image through employing DBSCAN, and carrying out the segmentation of the defect of a photovoltaic module through employing a DBSCAN algorithm. By determining two hyper-parameters, namely a domain radius Eps and a minimum point number MinPts in the domain radius, the DBSCAN can effectively capture a defect part with a complex shape; classifying and screening the clustering centers according to the relative positions of the clustering centers on the EL image, and taking a minimum rectangular frame capable of covering all reserved clustering centers as a fuzzy prompt frame of the defect; a side network is added on the basis of a segmented large model SAM, features in an image encoder and a decoder are aligned and fused, and high-quality segmentation of photovoltaic defects is realized through fine adjustment of the network. According to the method, the segmentation precision of the EL image under the large size is improved by improving the segmentation large model SAM, and a high-quality segmentation model is provided for a photovoltaic system.

The invention discloses a high dynamic range video tone mapping method, device and equipment, and the method comprises the steps: carrying out the display preprocessing of a target high dynamic range video frame image, and obtaining a linear light image; processing the linear light image by using a Retinex algorithm to obtain an illumination image; estimating a reflection map according to the illumination map by using an illumination reflection theory; converting the illumination image to a frequency domain, and performing spectral analysis to obtain a first radial average power spectrum; converting the reflectogram into a frequency domain, and performing spectral analysis to obtain a second radial average power spectrum; determining homomorphic filtering parameters according to the first radial average power spectrum and the second radial average power spectrum; processing the linear light image by using an illumination reflection model, and converting an obtained processed time domain signal into a frequency domain to obtain a processed frequency domain signal; and performing filtering processing on the processed frequency domain signal according to the homomorphic filtering parameter, and performing tone mapping on an obtained filtering result. According to the invention, detail loss and visual distortion are avoided.

The invention discloses a crack image extraction method, device and equipment, and relates to the field of crack detection of engineering structure materials, and the method comprises the steps: carrying out the graying processing of a first digital image, and determining a digital image after the graying processing; processing the grayed digital image according to non-local mean filtering to obtain a filtered digital image; performing homomorphic filtering processing on the filtered digital image by using a homomorphic filtering algorithm to obtain a second digital image, performing dilation operation, connecting discontinuous cracks of the second digital image, and determining a continuous second digital image; performing segmentation operation and denoising on the continuous second digital image, and determining the denoised second digital image; refining lines or foreground pixel contours in the skeleton into a single-pixel width skeleton; and quantitative analysis is carried out on the crack of the single-pixel width skeleton, and the crack images of the rock mass and the concrete material are extracted, so that the precision of crack extraction of the rock mass and the concrete material is improved.

The application discloses an image processing method applied to color difference detection of printed matter surfaces, and relates to the technical field of image processing, which comprises the following steps: acquiring a digital image of printed matter to be detected and performing non-uniformity correction of illumination, and converting the corrected digital image to a CIELAB color space; performing region segmentation on the digital image based on double constraint conditions of color gradient and texture boundary to generate a detection region map; extracting feature parameters based on the detection region map to generate a feature data set; establishing a dynamic reference model by using process parameters and material characteristics of the printed matter; calculating the distance between the feature data set and the dynamic reference model, identifying a color difference region, and generating a color difference score; and screening and grading the color difference region according to the color difference score. The application adopts a light correction technology combining a multi-layer image pyramid and a homomorphic filter, and introduces an adaptive weight fusion mechanism based on image features, so that the non-uniformity of illumination is effectively eliminated while the clarity of printed details is maintained.

The application is an underwater image enhancement method based on frequency domain and space domain joint processing, comprising: collecting public underwater image data set, searching the optimal background area of the collected underwater image by using the quadtree method, and then obtaining the maximum attenuation channel and the minimum attenuation channel difference of the optimal background area; comparing the attenuation channel difference distribution interval statistics with the color corrected image channel difference to obtain the underwater image color deviation factor threshold; using the improved channel compensation method to compensate different color deviation images to realize image color correction; using the improved homomorphic filtering method to perform contrast enhancement processing on the image; finally, fusing the color corrected image and the contrast enhanced image in the Lab color space to obtain the final color corrected and clear underwater enhanced image. The application realizes adaptive correction of underwater image color, contrast enhancement, detail enhancement and color restoration of underwater image.

The invention discloses a complex field environment-oriented tobacco disease multi-scale detection method. The method comprises the following steps: S1, constructing a multi-environment tobacco disease image data set; s2, preprocessing the image, including frequency domain homomorphic filtering, guide filtering and Poisson image editing, so as to overcome complex field environment interference; s3, constructing an improved target detection model oriented to multi-scale disease features, replacing a backbone network with MobileNetV3, introducing GhostNet and ECA attention modules into a neck network, and performing weight sharing optimization on a detection head, so as to enhance the perception ability of tiny disease spots and morphological diversity; s4, training the model by using a joint loss function and a multi-scale training strategy; s5, the model is optimized and then deployed to edge computing equipment, and real-time detection is achieved. According to the method, the generalization ability in a complex field environment is enhanced, the model calculation complexity and parameter quantity are effectively reduced, and the method is suitable for field real-time disease inspection and early warning.

The present invention discloses a high-order spectrum-driven homomorphic filtering high-resolution seismic data processing method. First, a seismic record is input, and a spectral simulation method is used to estimate the amplitude spectrum of the seismic wavelet from the seismic record. The third-order cumulants of the seismic record are calculated and Fourier transformed to obtain its phase spectrum. The phase spectrum of the mixed-phase wavelet at the separation point is calculated. A particle swarm algorithm is used to determine the optimal separation point between the seismic wavelet and the reflection coefficient sequence from the reciprocal spectrum sequence of the seismic record. The mixed-phase seismic wavelet is separated from the seismic record, and the seismic record is subjected to mixed-phase wavelet deconvolution to obtain an output record of the mixed-phase wavelet deconvolution. The method of the present invention reduces the complex high-dimensional functional inversion problem to a single-parameter inversion problem through the combined application of homomorphic filtering and high-order spectral transformation, thereby improving the stability and calculation accuracy of the mixed-phase wavelet estimation and the resolution of the seismic record, and enhancing the ability of the mixed-phase wavelet deconvolution method to reveal thin underground layer structures.