176 results about "Lab color space" patented technology

The invention discloses a product surface paint layer color difference detection method and system. The method comprises the following steps: converting an original image from an RGB color space to an LAB color space; then foreground segmentation is carried out to realize extraction of a to-be-detected object; lAB values of foreground pixels in the LAB image are extracted, a K-means algorithm is used for clustering the LAB values of the foreground pixels, and color grading of the foreground pixels is achieved; using a region merging denoising function to optimize the clustering labels, eliminating noise points with too small areas, and enabling the noise points to be automatically fused into regions with peripheral color levels; chromatic aberration between the clustering centers is calculated, and clustering center pairs with the distance larger than a threshold value serve as significant chromatic aberration pairs; and covering masks of different colors in each stage of color area, and generating a visual result of color grading and a visual result of an area with remarkable color difference. According to the invention, accurate and efficient image color difference detection is realized, and the method has a wide application prospect.

The invention provides a three-dimensional setting printing correction method and device, equipment and a storage medium, and the method comprises the steps: scanning a three-dimensional setting, and obtaining a three-dimensional point cloud model, color grid data and a multispectral texture mapping graph; based on the three-dimensional point cloud model and the color grid data, determining an offset defect labeling graph and a parallax offset matrix; performing two-dimensional mapping and global correlation calculation on the multispectral texture mapping graph to generate a texture anomaly thermodynamic graph and an LAB color space deviation vector; inputting the parallax offset matrix, the offset defect labeling diagram, the texture anomaly thermodynamic diagram and the LAB color space deviation vector into a preset decision tree model, and generating a root cause classification label and a printing quality score; and based on the root cause classification label and a preset historical printing parameter database, constructing a reinforcement learning model, taking the printing quality score as a reward function of the reinforcement learning model, and outputting a printing correction scheme.

The invention relates to the technical field of image processing, in particular to a printing image-text defect detection method based on machine vision, and the method comprises the steps: obtaining an RGB image of a to-be-detected printed matter, and carrying out the Lab space conversion, and obtaining a target image; performing normalization processing on the L channel value of each pixel point in the target image to obtain a normalized brightness value so as to divide the pixel points in the target image into a preset number of illumination level intervals; obtaining a color fluctuation statistical parameter of any one illumination level interval, and obtaining an optimal color difference threshold value corresponding to any one illumination level interval; according to the optimal color difference threshold value corresponding to the illumination level interval to which each pixel point in the target image belongs, performing Lab color space template comparison on the target image to obtain an image-text color difference defect result of the to-be-detected printed matter, thereby improving the accuracy of image-text printing defect detection based on the color difference threshold values.

The invention relates to the technical field of machine vision and optical detection, in particular to a tea color automatic evaluation method and system based on machine vision. The method comprises the following steps: constructing a stable and repeatable optical measurement environment and calculating radiation baseline parameters; performing multi-band polarization sequence imaging on the tea sample to obtain original data with spectrum and structural characteristics; separating mirror surface and diffuse reflection components on the surfaces of the tea leaves, and performing spectrum consistency correction by combining a radiation baseline; performing multiband spectrum reconstruction and normalization processing on the basis of the corrected diffuse reflection image, mapping to a CIE Lab color space, extracting pixel-level brightness, chromaticity and color distribution statistical indexes, and generating a color feature vector in combination with spatial weighting matrix optimization; a final score is calculated by constructing a quantitative evaluation and grade judgment model and integrating perception color separation, drying calibration color separation and a penalty term, and a grade result is output. According to the method, objective, accurate and automatic evaluation of the color of the tea leaves is realized.

The invention relates to a micro-expression recognition method based on double-flow feature fusion, and belongs to the field of computer vision. The method comprises the steps of obtaining N frames of images of a micro-expression video from a start frame to a vertex frame, and calculating an optical flow field between adjacent frames by adopting an optical flow algorithm so as to effectively extract optical flow features of micro-expressions; the method comprises the following steps: acquiring N frames of images of a micro-expression video from a start frame to a vertex frame, converting the N frames of RGB face images into a CIE Lab color space, calculating a pixel difference between two frames of converted Lab images, and extracting pixel stream features of micro-expressions; and inputting the optical flow features and the pixel flow features into a constructed double-flow three-dimensional convolutional network for feature extraction and fusion, and classifying micro expressions. According to the method, the dynamic and subtle changes of the micro-expression are effectively captured by combining the optical flow and the pixel difference characteristics. By integrating spatial and temporal information, richer feature representations are provided. The improved attention mechanism further focuses on fine facial changes, and the accuracy of micro-expression classification is improved.

The invention provides a textile surface defect detection method and system based on image features, and relates to the technical field of textile surface defect detection.The method comprises the steps that an RGB image of a to-be-detected textile is obtained and zoomed to 512 * 512 pixels, and then at least five semantic tags are output through a semantic segmentation model; the method comprises the following steps: adopting hyperspectral imaging with the wavelength of 400-1700 nm and the resolution of 10 nm, calculating the pixel reflectivity through a reflectivity formula, comparing the reflectivity of 5-8 characteristic wavebands, marking as a pollutant if the average deviation exceeds a threshold value, matching the type and position of a stain with a spectral angular distance, and after a standard sample and a semantic region are converted into an LAB color space, extracting a minimum rotation enclosing rectangle and dividing a grid, invalid pixels are filled with 3 * 3 neighborhood effective pixel weighted mean values, and a grid LAB mean value is calculated. A grid pollutant pixel corresponding to an image to be detected is filled with a standard sample in a mean value mode, then channels A and B are clustered through a sliding window, a pilling area is preliminarily judged according to the standard deviation of a channel L, and the pilling area is confirmed by combining the structured light three-dimensional point cloud and comparing with a threshold value.

The invention provides a facial skin flaw enhancement method based on a Lab color space. The method comprises the following steps: firstly, acquiring an RGB face image and converting the RGB face image into a CIE Lab color space with uniform perception; then, performing differentiation treatment according to the manually selected skin flaw type: for the vascular flaw, extracting statistical characteristics of a component and driving adaptive nonlinear transformation, and generating a grey-scale map which highlights the red flaw; for pigment flaws, nonlinear transformation is carried out on the component L, and then collaborative linear weighting and feature amplification are carried out on the component L, the component a and the component b, so that a grey-scale map with highlighted pigment spots is generated. And finally, coloring the grey-scale map in the Lab color space through adjustable parameters to generate a high-contrast color enhanced image. The method overcomes the dependence on hardware and training data in the prior art, can clearly and adaptively enhance various flaws such as acnes, couperose streaks and color spots, shows robustness under different illumination, and can be widely applied to clinical beauty, later photography and real-time video processing.

The invention discloses a method for dynamically monitoring the drying quality of fruits and vegetables by fusing chromatic aberration-shrinkage rate analysis. The method comprises the following steps: placing a fruit and vegetable sample on a rotatable objective table, drying according to a preset program, and carrying out multi-angle shooting by using a digital camera according to a timestamp to obtain an RGB color image; the method comprises the following steps: preprocessing by an image analysis system, converting an image into a Lab chromaticity space, calculating a chromatic aberration value delta E and a comprehensive shrinkage rate S, integrating data to generate a change curve, and constructing an appearance form model. In actual drying, real-time data and the model are compared, and early warning is carried out when the color difference value exceeds 3.5 or the shrinkage rate deviation exceeds 15% and image acquisition is abnormal. The drying equipment is provided with a high-precision electric rotary objective table, a specific annular light source array, a high-resolution camera and a standard calibration color card, and illumination and color measurement accuracy is ensured through calibration. Through multi-step operation, accurate and dynamic monitoring of fruit and vegetable drying quality is achieved, and drying process optimization is assisted.

The invention relates to the technical field of LED control, and discloses a multi-mode LED swimming pool lamp control method, system and device. The method comprises the following steps: calculating switching path pre-calculation data based on a remote controller mode switching instruction and color characteristic parameters of E light modes; according to the switching path pre-calculation data, carrying out progressive modulation on the RGBW four-channel PWM duty ratio to obtain a linear transition modulation sequence; on the basis of the linear transition modulation sequence, trajectory calculation is carried out on dynamic change mode color change in a CIE Lab color space, and color transition trajectory parameters are obtained; and closed-loop control is carried out on RGBW four-channel LED currents according to the color transition track parameters, PWM control signals are obtained, progressive adjustment of the PWM duty ratio is achieved, and the visual abrupt sense and the flicker phenomenon caused by traditional abrupt change type switching are eliminated.

The invention discloses a nozzle voltage adjustment method and device based on camera automatic identification printing color blocks and a storage medium, and relates to the technical field of printing device calibration, and the method comprises the steps: controlling a printing device to output a pattern containing each nozzle independent test area and color blocks; acquiring chromaticity distribution data through an image acquisition device, and extracting characteristic parameters such as brightness and saturation; determining a reference characteristic parameter based on a reference selection rule, and calculating a deviation value between each test area and the reference; an adjustment instruction is generated according to the mapping relation between the deviation value and the voltage adjustment amount, verification printing and iterative optimization are carried out after implementation until the precision is met, and through dynamic selection of a reference color block, LAB color space chromatic aberration calculation, voltage sensitivity coefficient matrix mapping and aging compensation, the adjustment precision and efficiency are improved, the dependence on artificial experience is reduced, and the adjustment precision and efficiency are improved. The method is suitable for nozzle calibration of high-precision printing equipment.

The invention provides a tomato leaf health status assessment system and method, and relates to the technical field of tomato leaf health assessment, and the method specifically comprises the steps: firstly, carrying out the preprocessing of a tomato leaf image, converting the image into a Lab color space, and carrying out the segmentation and extraction of a leaf region based on a chromaticity threshold value; and then leaf area chromaticity channel statistical magnitude is counted, pixel-level anomaly detection is carried out through an adaptive threshold value, and an abnormal area mask and a normal area mask are generated. Performing connected domain analysis on the abnormal region, extracting local color and texture features, and extracting corresponding features of the normal region as a reference; the health deviation degree is obtained by calculating the feature distance between each abnormal region and the normal region, and the comprehensive health assessment index is generated by combining the area proportion of the abnormal regions after normalization. And finally, judging a health state according to a preset threshold rule and outputting an evaluation result. According to the invention, a comprehensive and accurate decision basis is provided for agricultural producers, and the automation level and evaluation accuracy of tomato disease monitoring are improved.

The invention discloses an art design intelligent color matching auxiliary system and method based on artificial intelligence, and relates to the field of art design. Comprising an image analysis module, and the image analysis module comprises a noise reduction preprocessing unit for carrying out noise suppression on an input image by adopting a bilateral filter and a semantic segmentation unit for analyzing an image semantic region and outputting a segmentation mask by using a DeepLabv3 + model; a color space conversion unit that inputs and converts the RGB image into a CIELAB space; a style feature extraction unit; a palette optimization module; and a rendering output module. According to the method, the four-dimensional statistical vector of the LAB color space is extracted, high-level features such as the quantized lightness centrality, the chroma discreteness and the distribution skewness are extracted, the problem of feature expression distortion of a traditional method is solved, the style loss function based on the Gram matrix is constructed, gradient descent optimization is combined, and it is ensured that the palette meets the aesthetic rule and style consistency at the same time.

The invention discloses a sandstone slice image component identification method based on a GL-SLIC algorithm and semi-supervised learning. The method comprises the following steps: firstly, acquiring a sandstone slice image amplified by 200 times under single polarization, converting an RGB image into a CIE-Lab color space, and extracting a GLBP texture feature vector; then a GL-SLIC superpixel segmentation algorithm is constructed, and adaptive segmentation based on texture and color features is realized in combination with a Gabor filter and a local binary pattern; then implementing a region merging algorithm to generate complete mineral particles and pore fragments; finally, a classifier based on VGG16 and a discriminator based on ResNet18 are constructed, a semi-supervised self-training framework is adopted, a model is initialized by using about 6% of manual labeling samples, a high-confidence-coefficient pseudo-label extension training data set is generated through iteration, and automatic recognition of sandstone components such as quartz, pores, kaolinite, rock debris and a matrix is achieved. According to the method, the recognition accuracy of 96.3% on a test set is achieved, compared with a traditional method, the segmentation precision and the recognition accuracy are remarkably improved, the data labeling cost is greatly reduced, and an efficient technical scheme is provided for automatic analysis of geological images.

The invention discloses a fabric texture fidelity rendering method and system based on a scanned image and a target color number, and solves the technical problems of poor color perception consistency and texture detail loss. The method comprises the steps of preprocessing a standardized input image through illumination compensation, performing double-space color mapping in an LAB color space, partitioning the image by adopting K-means clustering according to texture features of a gray-level co-occurrence matrix, establishing a color mapping function containing global mapping transformation and local adaptive adjustment, and adaptively determining a fusion coefficient according to regional variance; decomposing the image into a base layer and a detail layer by adopting guide filtering, and performing spectrum constraint on the detail layer to ensure that the spectrum amplitude is consistent with the original; extracting material parameters such as a normal map, a roughness map and a metallity map; according to the method, fabric texture details are completely reserved while color perception consistency is guaranteed, rendering reality is improved, batch processing of multiple groups of target color numbers is supported, and the method is suitable for application scenes such as costume design, e-commerce display and virtual fitting.

The invention relates to the field of computer vision, and discloses an image classification and defect detection method based on self-supervised contrast learning, which comprises the following steps: converting an image to be detected into an LAB color space, and generating feature representation information of principal component analysis based on separated information of a channel A, a channel B and a channel L; extracting a target potential feature vector from the feature representation information through a first encoder; extracting high-level semantic information from the target potential feature vector through a first semantic feature encoder; performing classification detection on the advanced semantic information through a classifier network; wherein the construction of the first encoder and the first semantic feature encoder comprises the following steps: respectively inputting a training sample set into the first encoder and the second encoder; and performing parameter optimization on the first semantic feature encoder and the second semantic feature encoder by adopting a comparative learning mode.

The invention discloses a line sequence automatic detection algorithm based on machine vision and an implementation method thereof, and belongs to the technical field of machine vision and image processing. Aiming at the problems of low efficiency and poor accuracy of traditional manual detection of a multi-core cable, the invention provides the following technical scheme: preprocessing a collected cable image, and enhancing the image by adopting adaptive histogram equalization of an LAB color space; determining a scanning band at the center of the image and converting the scanning band to an HSV color space; the comprehensive value of the HSV color gradient is calculated, the peak value is detected by adopting an adaptive threshold value, and the boundary of each wire core is accurately positioned; establishing an HSV (hue, saturation and value) feature library containing 10 standard colors, and realizing color matching through weighted distance measurement; and an automatic detection mode and a manual assistance mode are provided. According to the method, the detection accuracy reaches 95% or above, the processing time of a single image is shorter than 1 second, the technical problems of automatic color recognition and sorting detection of the multi-core cable are effectively solved, and the method can be widely applied to cable production quality control.

The invention relates to the technical field of image data processing, in particular to a production speed regulation and control method and system for packaging bag printing. The method comprises the following steps: acquiring and segmenting a printed packaging bag image, and correcting rotation offset by using a histogram of oriented gradients to ensure image alignment. Then, the corrected image is converted into an LAB color space, the LAB color space is compared with a preset standard template, the ink color unevenness is calculated, and the printing quality is quantified. And finally, a relation model of the printing quality and the production speed is constructed based on historical data, the target production speed is determined, regulated and controlled, and balance of the printing efficiency and the quality is achieved. According to the invention, the influence of artificial experience dependence and image dislocation on quality evaluation is overcome, and the accuracy and stability of printing speed regulation and control are improved.

The invention relates to the technical field of batteries, in particular to a battery collector plate and a battery. The battery collector plate comprises copper and an electric connection part, and the electric connection part is used for realizing electric connection between a battery cell tab and an electrode terminal; the color parameters of the surface, used for being connected with the battery cell tab and / or the electrode terminal, of the electric connection part comprise L1 and a1, L1 is the numerical value of an L channel in the Lab color space, a1 is the numerical value of an a channel in the Lab color space, S = L1a1, and S is larger than or equal to 900 and smaller than or equal to 2500. By controlling the product S of L1 and a1 on the surface of the electric connection part to be within a proper range, light reflection can be reduced, the welding quality and stability are improved, and the structural strength of the battery collector plate is ensured.

The present invention discloses a non-contact heart rate estimation method and system based on multimodal signal fusion, which relates to the field of image processing technology. The method includes: locating the region of interest in a face video, extracting and fusing HSV and Lab color space signals; suppressing ambient light interference through background brightness compensation to obtain a color conversion signal and normalizing it; using historical heart rate to adjust the bandwidth of a bandpass filter to filter the signal to generate an rPPG signal; performing time domain and frequency domain analysis on the rPPG signal, combining the two results and taking a weighted average to obtain a heart rate estimate; and using a historical data smoothing strategy to correct the heart rate estimation result to ensure accuracy. The present invention achieves high-precision non-contact heart rate estimation by extracting and fusing HSV and Lab color space signals, background brightness compensation, signal normalization, and processing the face video signal based on the historical heart rate adjustment bandpass filter.

The application discloses a colonoscopy image data enhancement method, and belongs to the technical field of computer vision, which comprises the following steps: acquiring multiple original colonoscopy images, fusing the gray information and the saturation information to obtain a set of coordinate points of the reflection area of the original colonoscopy images; calculating the set of coordinate points of the small reflection area from the set of coordinate points of the reflection area and restoring the set of coordinate points to obtain a small reflection area restoration image; converting the source image and the target domain image to the LAB color space; calculating the standard deviation and the mean value of each color channel of the source image and the target domain image except the reflection area and the dark area; migrating the color domain of the source image to the target domain corresponding to the target domain image; restoring the dark area of the migration image to obtain a color domain migration enhancement image; and composing the original colonoscopy image, the small reflection area restoration image and the color domain migration enhancement image into a colonoscopy enhancement image data set, wherein the colonoscopy enhancement image data set is used for subsequent image analysis.

The application provides an attribute recognition method, device, equipment and medium. In the application, before attribute recognition is performed based on a pre-trained attribute recognition model, for each pixel point in a first to-be-recognized image, a ratio of a first variance of each channel of the first to-be-recognized image in a LAB color space to a pre-stored standard control variance, a pre-stored control value of a standard control pixel point corresponding to the pixel point in each channel, and a first mean value of each channel of the first to-be-recognized image in the LAB color space are determined, a first corrected value of each channel of the first to-be-recognized image in the LAB color space corresponding to each pixel point is determined, a first target to-be-recognized image in a corrected RGB color space is determined based on each first corrected value, attribute recognition is performed based on the first target to-be-recognized image, and the accuracy of attribute recognition is effectively improved.

This invention provides a machine vision recognition method and system for commercially available frozen mutton slices adulterated with other meats, relating to the fields of food quality authenticity detection and computer vision technology. The invention acquires images of frozen mutton slices under standardized visible light imaging conditions, and obtains a preprocessed image set through unified resolution, normalization, circular region of interest extraction, and data augmentation. It constructs color matrix features in RGB, HSV, and Lab color spaces and generates five texture features from gray-level co-occurrence matrices, and extracts principal component features with a cumulative contribution rate exceeding 99% through principal component analysis. A random forest model is trained based on original-cut, conditioned, and reconstituted mutton slice samples to classify and identify the mutton slices to be tested. This invention achieves rapid, stable, and high-precision identification of frozen mutton slices adulterated with foreign meat and meat products under standardized illumination conditions.

The application provides a style transfer strong similarity-based generation method, device, equipment and medium, the method comprising: S10, redrawing a style reference map, outputting a clear style map; S20, inputting the clear style map into a monocular depth estimation model, outputting a single-channel depth map; S30, inputting the single-channel depth map into an enhanced image generation model for structure optimization, generating a redrawing sketch; S40, using a visual feature extraction model and a deep learning model to perform style fusion and rendering on the redrawing sketch, generating a preliminary style map; S50, pre-training a LoRA model, using the LoRA model to adjust the preliminary style map, outputting an intermediate style map; S60, after upsampling the intermediate style map, performing block optimization, outputting an optimized style map; S70, inputting the optimized style map into a Lab color space, performing histogram matching, and generating a final style map.

The embodiment of the invention provides an image processing method and device and a readable storage medium. The method comprises the following steps: acquiring a first image and a second image, wherein the second image is an image obtained by performing style editing processing on the first image; performing Lab color space conversion on the first image and the second image to obtain a first brightness channel and a first color channel of the first image and a second brightness channel and a second color channel of the second image; fusing and filtering the first brightness channel and the second brightness channel to obtain a third brightness channel; based on the pixel correlation of the first brightness channel and the second brightness channel, reconstructing the second color channel to obtain a third color channel; and outputting a fused image based on the third brightness channel and the third color channel. The fused image keeps the style of the second image and keeps the texture features of the first image, and detail distortion is avoided.

The present application relates to the technical field of thickness detection, and discloses a catenary dropper icing thickness monitoring and early warning method and system, which comprises the following steps: obtaining a to-be-detected image of a catenary dropper, converting the image to HSV and Lab color spaces, and generating a fused edge feature map; segmenting out an icing area of the dropper, and extracting a pixel width sample set; fitting the pixel width sample set, obtaining an initial icing thickness probability distribution, and extracting a measurement uncertainty factor; constructing a historical icing thickness prior distribution; determining a current measurement confidence, and calculating a fusion coefficient; performing conjugate updating on the initial icing thickness probability distribution and the historical icing thickness prior distribution, obtaining an icing thickness posterior probability distribution and a distribution variance; obtaining a real-time environmental wind speed, calculating a safety thickness threshold and an early warning probability threshold; and when the cumulative probability of the icing thickness posterior probability distribution in the part exceeding the safety thickness threshold is greater than the adjusted early warning probability threshold, outputting an early warning signal. The present application improves the accuracy of the icing thickness monitoring result.

The application provides a cloud image efficient synthesis method, belonging to the field of image processing, comprising the following steps: calculating the edge distance transform of the mask of the foreground image to generate a multi-level boundary information graph; converting the background image and the foreground image to the Lab color space, and inputting the Lab channel, the mask and the multi-level boundary information graph into a generator network to output a preliminary fusion image in the Lab color space; using a brightness discriminator and a chroma discriminator to respectively judge the local texture consistency of the preliminary fusion image and a real fusion image in the L channel, and the global color harmony of the preliminary fusion image and the real fusion image in the a and b channels; performing the adversarial training of the generator network and the discriminator based on a total loss function until the network model converges, and using the converged generator network to generate a final fusion image. The application can guarantee the high realism and consistency of the fusion image in the details and the overall color tone.

The application discloses a bubble detection, tracking and parameter quantification method and device based on an improved YOLOv8. The method comprises the following steps: collecting continuous frame visible light RGB bubble images; converting the RGB bubble images into LAB color space, performing adaptive enhancement on an L channel, performing color balance on A and B channels, and obtaining a pretreatment image after standardization and normalization processing; inputting the pretreatment image into an improved YOLOv8 bubble detection model, setting a multi-scale feature enhancement module after a P4 layer of the model, and adopting a bidirectional feature fusion structure and a decoupling detection head to output a bubble detection result; performing cross-frame association based on continuous frame detection results to obtain a bubble trajectory sequence; and calculating bubble aspect ratio and motion speed parameters according to a detection bounding box and the trajectory sequence. The application is suitable for bubble detection, tracking and parameter quantification analysis in a complex background.

The invention discloses an image super-resolution reconstruction method and system, and belongs to the field of digital cultural heritage protection, and the method comprises the steps: constructing a multi-resolution image pyramid, carrying out the semantic analysis based on a low-resolution layer, recognizing a key structure and a uniform region, and generating a self-adaptive dicing strategy; dynamically selecting a super-resolution model in combination with texture complexity grading through streaming loading of the blocks and the overlapping bands to a GPU (Graphic Processing Unit); and executing global color micro-correction in a Lab color space, and generating a seamless high-definition image by applying Poisson fusion in an overlapping zone region. According to the method, boundary artifacts, texture fractures and color deviations caused by blocking processing can be eliminated, video memory occupation is reduced, and calculation efficiency is optimized.

This invention relates to a urine test strip color recognition detection method based on image analysis, comprising the following steps: Step 1, acquiring a color image; Step 2, detecting black calibration blocks at the four corners; Step 3, geometric correction of the test strip area based on four-point perspective transformation; Step 4, adaptive extraction of the effective area of ​​the test strip; Step 5, precise segmentation of color blocks; Step 6, correction of uneven illumination and color standardization; Step 7, multidimensional similarity calculation based on weighted Lab color space; Step 8, outputting the detection results and marking abnormalities. The beneficial effects of this invention are: the positioning accuracy remains ≥99.8% even in complex scenarios, far exceeding existing template matching and QR code solutions; it is completely unaffected by test strip deformation or printing offset; the CV value of the same urine sample repeated under different mobile phones and different lighting conditions is <3%; it successfully solves the problems of batch-to-batch color difference and test strip aging, with a clinical concordance rate ≥98%; the entire algorithm has a small computational load, fully meeting the needs of real-time detection.

The invention provides an efficient cloud image synthesis method, which belongs to the field of image processing and comprises the following steps of: calculating edge distance transformation for a mask of a foreground image to generate a multi-level boundary information graph; converting the background image and the foreground image into a Lab color space, inputting the Lab channel, the mask and the multi-level boundary information graph into a generator network together, and outputting a preliminary fusion image in the Lab color space; a brightness discriminator and a chrominance discriminator are adopted to discriminate the local texture consistency of the preliminary fusion image and the real fusion image in the L channel and the global color harmony of the preliminary fusion image and the real fusion image in the a channel and the b channel respectively; and performing adversarial training on the generator network and the discriminator based on the total loss function until the network model converges, and generating a final fusion image by using the converged generator network. According to the method, the high reality sense and consistency of the fused image in detail texture and overall hue can be ensured.