15 results about "Homomorphic filtering" patented technology

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 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.

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 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 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 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 invention discloses a method for judging and optimizing image quality, and relates to the technical field of image processing. The method comprises the following steps: S1, carrying out target detection on an input image by utilizing a target detection model, and identifying a key target area in the image; s2, extracting image data of the key target area according to a target detection result; s3, carrying out histogram analysis on the key target area, carrying out statistics on gray level pixel distribution, and judging the exposure state of the image based on the ratio of high-brightness pixels to low-brightness pixels; and S4, selecting a corresponding image optimization algorithm for processing according to the exposure state. According to the method, the key target in the image can be accurately extracted, the image quality is judged according to the target detection result, the definition and detail integrity of the key target in the image are ensured, and fine exposure adjustment is carried out on the target area in combination with homomorphic filtering and an adaptive histogram equalization technology.

The application discloses a method and system for detecting water stains and drug residues of a mask, and relates to the field of mask defect detection, and solves the problem of insufficient detection accuracy when detecting water stains and drug residues of a mask. The technical scheme is as follows: image data of the mask is collected; the image data is subjected to light calibration to obtain a brightness correction image; the brightness correction image is subjected to homomorphic filtering to compress the brightness range of the brightness correction image and enhance the contrast of the brightness correction image, thereby obtaining an enhanced image; the enhanced image is subjected to bilateral filtering to remove interference noise of the enhanced image and retain edge characteristic values of the enhanced image, thereby obtaining a result image; a preset AOI logic algorithm is used to perform defect determination on the result image, thereby obtaining a defect area in which water stains and drug residues may exist; and the defect area is subjected to defect re-inspection, and a defect detection result of water stains and drug residues is output.

The invention discloses an intelligent agricultural product sorting method based on physical feature iterative optimization and a related device, and belongs to the technical field of agricultural product sorting. The method comprises the following steps: collecting a surface reflection image and a near-infrared transmission image of an agricultural product to be sorted; using homomorphic filtering to carry out anti-wrinkle preprocessing on the surface reflection image; extracting physical feature vectors of the two images; iterative optimization is carried out based on the physical characteristic matrix, an optimal judgment threshold parameter combination is obtained, feature extraction and judgment are carried out on agricultural product images collected in real time, and an execution mechanism is controlled to complete the sorting action according to encoder signals and a delay compensation mechanism. According to the method, morphological interference is solved through homomorphic filtering, interpretability detection of quality is achieved through white-box physical characteristic quantification, top-speed self-adaption of parameters is achieved through matrix iteration, and therefore precise and intelligent sorting of complex agricultural products is achieved.

This invention discloses an intelligent quantification method for borehole optical images for cutoff wall quality assessment, belonging to the field of cutoff wall quality technology. It aims to solve the problems of difficulty in identifying micro-cracks, strong subjectivity in quantitative judgment of geometric features, and low detection efficiency in manual inspection. The method includes the following steps: acquiring high-resolution images of the borehole wall; eliminating specular reflection through adaptive homomorphic filtering to generate an illumination-invariant image; inputting a U-Net++ network (crack segmentation model) with a fused directional gradient histogram enhancement module to identify crack pixels with a width <0.1mm; calculating the length, bifurcation angle, and dip through crack topological connectivity analysis; predicting potential penetration paths using a soil stress field model; calculating an index characterizing the integrity of the cutoff wall based on the segmentation results, geometric features, and potential paths; and generating a crack risk assessment report based on this index.

Disclosed is a feature fusion classification method for multiple types of packaging bag, relating to the technical field of classification of multiple types of packaging bag. On the basis of a random forest concept, the present invention provides a feature fusion classification algorithm for multiple types of packaging bag. By means of three different classification methods: a support vector machine, template matching, and a neural network, denoising processing is performed on images of various packaging bags transmitted from a camera using a median filter, and on the basis of a homomorphic filtering algorithm, enhancement processing is performed on the denoised images. The images are classified by separately using a support vector machine model, a template matching algorithm, and a neural network model, and a majority rule-based voting mechanism is implemented for prediction results of the three methods, to obtain a final result. The voting mechanism-based feature fusion classification algorithm for multiple types of packaging bag of the present invention provides high accuracy, reduces error generation, and obtains more accurate results.

The present invention belongs to the field of electronic countermeasure technology, and discloses a method, system for individual identification of communication radiation sources based on time-frequency filter coefficient fusion. The method includes: using a constant modulus algorithm in the time domain to perform equalization filtering on the transmitted signal carrying the radiation source fingerprint, extracting the equalizer coefficients after equalization convergence; converting the equalized, filtered signal to the frequency domain, applying a Mel filter bank to perform homomorphic filtering on the signal in the frequency domain to obtain the filtered Mel filter coefficients; performing frame-wise convolution on the equalizer coefficients, the Mel filter coefficients to fuse the time-domain, frequency-domain features of the communication radiation source, extract the communication radiation source fingerprint; constructing an individual identification model for communication radiation sources; identifying, classifying the extracted communication radiation source fingerprint features based on the individual identification model for communication radiation sources.