30 results about "Exposure fusion" patented technology

The invention discloses a hand-eye-force integration-based catenary cantilever robot assembly method, which comprises the following steps of: synthesizing image sequences of different exposures into a high-quality image based on a self-adaptive multi-exposure fusion technology, identifying a workpiece contour in the image based on a deep learning framework, and calculating a pose of an identified part in a camera coordinate system; the two-dimensional camera and the three-dimensional camera are matched to carry out accurate scanning, an accurate pose deviation matrix of an actual workpiece relative to a theoretical model is obtained through a nearest point cloud registration algorithm, and a corrected moving target pose is calculated; and the robot moves towards the position and posture of the moving target, when the robot is about to make contact with the assembly workpiece, the impedance control mode is switched to conduct cantilever installation, and moment data are continuously monitored in the whole process till installation is completed. According to the method, the robot can intelligently cope with uncertainty in the cantilever assembly process, and the assembly success rate and the assembly efficiency are remarkably improved.

The invention discloses a bridge underwater safety detection method based on opto-acoustic dual modes, and relates to the technical field of underwater engineering detection, and the method comprises the following steps: obtaining water area particle size distribution and concentration data by using a multi-frequency acoustic suspended sediment concentration profiler, establishing an environmental parameter database, configuring a shipborne detection device according to the initial environmental parameter database, and detecting the underwater safety of a bridge according to the shipborne detection device. The method comprises the following steps: setting a three-axis stabilized platform carrying a polarization imaging camera and a multispectral LED array, deploying a multi-beam detection module and ADCP equipment, and establishing a unified coordinate system; multi-angle original intensity images are collected through a polarized optical imaging module, and multi-exposure fusion is carried out after processing is carried out through a mutual information minimization algorithm. An optical image and an acoustic point cloud are deeply fused by adopting a graph neural network, the detection precision is improved through dynamic weight optimization of an attention mechanism and a space-time registration algorithm, a multi-index fusion model is constructed based on fuzzy logic and a D-S evidence theory for adaptive early warning decision, upgrading from single-parameter alarm to multi-dimensional decision is realized, and the false alarm rate is reduced.

The invention discloses a fringe projection measurement method for a high dynamic range scene. The fringe projection measurement method can realize high-precision three-dimensional reconstruction of a complex reflection scene without human intervention. The method comprises the following steps: firstly, projecting pure gray-scale maps with different gray scales to calculate the maximum unsaturated exposure time of a camera for a scene, constructing a phase error model for a fringe pattern captured under exposure, and respectively calculating the optimal exposure time of each pixel point under phase error and light intensity constraint conditions; and then, obtaining required exposure times, masks and exposure time by using an iterative optimization algorithm of the masks. And finally, obtaining an optimal fringe pattern for three-dimensional reconstruction by automatically capturing and fusing fringe patterns at different exposure times. Compared with a traditional high-dynamic measurement method, the method has the advantages that high-quality multi-exposure fusion can be completed without human intervention, and high-precision and high-robustness three-dimensional measurement of a high-dynamic-range scene is realized.

The application discloses a kind of based on multiple exposure generation fusion's low-light image enhancement system, belong to low-light image enhancement technical field.System includes: adaptive multiple exposure adjustment module (AMEA), joint prediction gamma coefficient, linear modulation factor and compensation parameter, to input low-light image carries out nonlinear and linear combination transformation, generates multiple luminance, contrast and different color response multiple exposure candidate image;Multiple exposure fusion module (MEF), original low-light image and candidate image channel splicing are carried out, and preliminary enhancement image is obtained by fine-grained weighting fusion of channel fusion coefficient and introduction of perception compensation parameter correction;Color refinement module (CRM), using encoder decoder structure and introducing fast fourier representation in bottleneck layer, realize spatial domain and frequency domain joint modeling, carry out global color calibration and local detail optimization to preliminary enhancement image.The overall network uses lightweight design such as depth separable convolution and dense connection, inference is simple, and can be run in real time in edge device.The application generates by adaptive multiple exposure, structure constraint fusion and frequency domain refinement, effectively restores dark texture, suppresses over-enhancement and color deviation, improves visual naturalness and robustness in complex low-light scene.

The invention discloses a simulation-based high dynamic range image synthesis method, which comprises the following steps of: generating low dynamic range images with different exposure degrees based on a CARLA simulator, and synthesizing a plurality of low dynamic range images into a high dynamic range image by using a multi-exposure fusion algorithm, thereby constructing a large-scale automatic driving high dynamic range simulation data set. According to the invention, through a multi-exposure fusion algorithm, low-dynamic-range images of multiple exposures are concisely and efficiently fused, a high-quality high-dynamic-range image which retains details of a dark part and a bright part at the same time is obtained, and the synthesis speed is relatively high. The method supports the synthesis of a large-scale automatic driving high-dynamic range policy data set, and is used for the research of related tasks.

The application discloses a kind of self-adapting multiple exposure fusion methods and systems based on surface structure light three-dimensional measurement, the method is first based on initial exposure image calculation modulation chart and maximum gray value, and according to modulation degree division multiple regions, and through the gray value correction and local information entropy weighting of pixel, the average gray of each clustering is accurately calculated, and then the optimal exposure time is solved for different regions, in image fusion stage, by analyzing the gray value distribution of high exposure image Dynamic determination of overexposure threshold, and with the pixel modulation degree under each exposure as the core basis, the fusion weight is calculated, and finally the global high-quality fringe image is generated.The application solves the problem that the reconstruction accuracy is low due to local overexposure or underexposure of high dynamic range objects, and significantly improves the three-dimensional measurement accuracy and automation level of high dynamic range objects.

The application provides a deep multi-modal image fusion method and system, and relates to the technical field of image processing; the method comprises the following steps: respectively extracting features of an overexposed image and an underexposed image, respectively obtaining dictionary features and sparse features of the overexposed image and dictionary features and sparse features of the underexposed image, updating the dictionary features and the sparse features of the overexposed image and the underexposed image through a total target function, respectively extracting common features from the updated dictionary features and the sparse features through a nonlinear function, extracting features according to the extracted common features, obtaining an overexposed fusion image and an underexposed fusion image and performing fusion processing, obtaining a reconstructed image and comparing the reconstructed image with a standard image to generate an evaluation index. The total target function is iteratively changed to perform transformation learning, higher-quality sparse features are extracted to capture important internal properties of the image to reconstruct the image, and the fusion of multi-modal images is realized.

The embodiment of the invention provides a multi-exposure image fusion method and device, electronic equipment and a computer readable storage medium, and relates to the technical field of image processing. According to the method, distinguishing and modeling of a moving object and a static background in a dynamic scene are achieved by generating a moving area weight map, a median threshold bitmap, a superpixel area map and a basic weight map. The median threshold bitmap and the superpixel area map are used for correcting the motion area weight map, the space consistency and the boundary fitting degree of motion detection are improved, motion interference in the basic weight map is eliminated through the final motion weight map, and an accurate fusion weight map is obtained. On this basis, multi-exposure fusion is completed in combination with an original image sequence, ghosting artifacts caused by object movement can be effectively suppressed while highlight and shadow details are reserved, and the quality and authenticity of the fused image are improved.

The invention relates to the technical field of optical imaging, in particular to an image highlight removing method and system based on virtual polaroid and virtual exposure fusion, and the method comprises the steps: obtaining a Stokes parameter image of a target scene, dividing highlight region blocks, preliminarily removing highlight of the highlight region blocks, and removing highlight in combination with a virtual exposure image. According to the method, the virtual polarization filtering technology and the virtual exposure fusion technology are creatively combined, the advantages of the virtual polarization filtering technology and the virtual exposure fusion technology are fully exerted, a brand-new and efficient image highlight removal method is formed, the projector and a light field camera do not need to be combined in the whole process, and the image highlight removal efficiency is improved. According to the invention, the method does not need to employ a plurality of light sources or detectors to obtain images under different visual angles or illumination conditions, overcomes the harsh implementation conditions of the traditional highlight removal technology, improves the reliability and uniformity of highlight removal, and solves the problems of harsh highlight region removal conditions and poor universality in the prior art.

The application relates to the field of medical image processing, in particular to an examination image processing method based on multi-exposure fusion. The method comprises the following steps: collecting multiple groups of images with different exposure intensities under the same examination visual angle; segmenting throat key anatomical structures in each group of images to obtain initial boundary coordinates; generating a triangular mesh covering the key region in the range of the initial boundary coordinates as a feature unit; calculating brightness feature values including brightness average values and brightness gradient values for the feature unit; establishing the association of the feature units among multiple images according to the brightness average values, and selecting the clearest expression of each boundary based on the brightness gradient values, and finally fusing all the optimized boundaries to form a strengthened result. The method significantly improves the detail performance of highlight and shadow areas in the image, effectively reduces the work burden of multi-image comparison and analysis while ensuring the diagnostic accuracy.

The invention discloses a high-reflection surface three-dimensional reconstruction method and device based on virtual exposure fusion. The method comprises the following steps: collecting a plurality of exposure image groups of a to-be-reconstructed target; one exposure image group corresponds to one projection frequency and comprises a plurality of exposure image subgroups; generating a corresponding intermediate exposure image group based on each exposure image subgroup, and then performing weighted fusion to obtain a corresponding virtual exposure image; fusing the virtual exposure image corresponding to each middle exposure image group and the high-exposure image and the low-exposure image in the corresponding exposure image subgroup by adopting a Laplacian pyramid multi-scale fusion mode to obtain a corresponding fused image; based on each fused image, obtaining an absolute phase distribution diagram through a phase shift method and a multi-frequency heterodyne method; and generating a three-dimensional point cloud of the to-be-reconstructed target according to the absolute phase distribution diagram, thereby providing a high-reflection surface three-dimensional reconstruction method which is wide in application range, simpler in process and higher in precision.

An exposure control method includes: calculating a long-exposure brightness mean value of pixels of darker (N−X) kinds of brightness among N kinds of brightness values of a long exposure image, wherein a total number of pixels of brightest X kinds of brightness values among the N kinds of brightness values is a long-exposure bright pixel number; when the long-exposure brightness mean value doesn't approximate to a long-exposure target brightness mean value, adjusting exposure setting for generating / modifying the long exposure image to make the two brightness mean values be similar / equal; determining brightest K pixels of a short exposure image, wherein the K is equal to the long-exposure bright pixel number; calculating a short-exposure brightness mean value; and when the short-exposure brightness mean value doesn't approximate to a short-exposure target brightness mean value, adjusting exposure setting for generating / modifying the short exposure image to make the two brightness mean values be similar / equal.

The application belongs to the technical field of image processing, and particularly relates to a multi-exposure fusion method for separating illumination and motion; low-exposure images, medium-exposure images and high-exposure images are subjected to illumination preprocessing and input into a fusion model; the low-exposure images and the high-exposure images are mapped to the illumination level of the medium-exposure images and normalized according to first gamma values and second gamma values, to obtain first new medium-exposure images and second new medium-exposure images, and the new medium-exposure images are subjected to motion alignment with the medium-exposure images; the first new medium-exposure images and the second new medium-exposure images are subjected to inverse gamma transformation respectively, to obtain new low-exposure images and new high-exposure images, and the new low-exposure images and the new high-exposure images are subjected to illumination fusion with the medium-exposure images to obtain a high dynamic range image, loss is calculated, and model parameters are trained by back propagation; a to-be-processed image is obtained, and a corresponding high dynamic range image of the to-be-processed image is obtained by using the trained fusion model; the application adopts the idea of separating illumination and motion, so that the high dynamic range image retains more information of the input image, and ghosting is reduced.

This invention relates to the field of image enhancement technology and discloses a low-light image enhancement method and system for 5G remote control systems based on deep Retinex decomposition. The method acquires and preprocesses the original low-light image from the front-end device of the 5G remote control system, inputs it into a lightweight multi-scale illumination sensing network to separate the initial illumination and reflection components, corrects the initial illumination component using a dynamic dual-branch exposure fusion module, processes the initial reflection component using a residual noise-detail co-optimization module, and then reconstructs and enhances the image through a halo and color joint correction mechanism. The image is then encoded, compressed, and transmitted to the remote control center via a 5G private network. This invention solves the core problems of underexposure and overexposure, noise interference, detail loss, and wasted bandwidth in low-light images in 5G remote control scenarios, achieving lightweight real-time enhancement at the edge, ensuring image quality and transmission efficiency, and providing reliable support for remote control decisions.

The invention relates to the technical field of image enhancement processing, and particularly provides a camera low-light image enhancement method and system based on dynamic exposure fusion deep learning, and the method comprises the steps: generating a group of control signals with continuous virtual exposure parameters, and driving an image signal processing program to simulate and generate a corresponding short-time image sequence; analyzing a signal reliability measurement diagram of each pixel position in the short-time image sequence under different virtual exposure parameters; performing selective aggregation on features of all corresponding pixel positions in the short-time image sequence in the feature domain to generate an intermediate fusion image; and interacting the features of the intermediate fusion image with a task feature map of an embedded external visual task priori knowledge parallel processing path to generate a group of task-perceived attention masks. The system comprises an image signal processing module, an intermediate fusion image module and a feature interaction module. According to the method, a complete processing link from physical signal simulation to semantic feature maintenance is formed.

The application relates to a low-illumination gray-scale image enhancement and denoising method, which comprises the following steps: constructing an optimal exposure ratio image according to an input low-illumination gray-scale image; decomposing the low-illumination gray-scale image and the optimal exposure ratio image by adopting a LatLRR method to obtain a low-rank part and a significant part; constructing a low-rank part weight and a significant part weight according to the obtained low-rank part and the significant part; performing normalization processing on the obtained low-rank part weight and the significant part weight to obtain a normalized weight; and performing image reconstruction and synthesis according to the low-rank part, the significant part and the normalized weight to obtain a multi-exposure fusion enhanced image. The application can enhance and denoise a single low-illumination image, restore structure information, maintain details, avoid block effects and reduce the calculation complexity.

The invention discloses a self-adaptive multi-exposure fusion three-dimensional measurement method for a high dynamic range scene. The method comprises the following steps: firstly, acquiring high-frequency phase shift fringe patterns at different exposure times, and finding out initial exposure time for preventing effective measurement areas in all phase shift patterns from overexposure through time domain light intensity overexposure detection operation; taking the high-frequency phase shift fringe pattern of the initial exposure time as an initial image of multi-exposure fusion, adaptively reserving a fringe region with high phase quality according to a modulation degree condition through a noise-modulation degree model, and introducing a time domain superposition principle to estimate the next exposure time of the multi-exposure fusion; and finally, calculating an effective measurement area of the current fused fringe image by using a reference plane phase based on depth constraint, and continuing to perform multi-exposure fusion until the effective measurement area meets a modulation degree condition, thereby completing three-dimensional measurement of the whole high dynamic range scene. According to the invention, the high-quality fringe pattern is obtained through adaptive multi-exposure fusion, the total exposure time is significantly reduced by introducing the time domain superposition technology, and high-precision and high-efficiency full-automatic three-dimensional measurement of a high-dynamic-range scene is realized.

The invention discloses a multi-exposure fusion angle resolution scattering micro-nano structure measurement method, and relates to the technical field of integrated circuit measurement. The method comprises the following steps: acquiring a multi-exposure sequence image set, and modeling a camera response function CRF; estimating an overexposed pixel value by adopting a linear extrapolation method based on a tangent line at the endpoint of a response curve, and calculating the confidence coefficient of an extrapolated pixel; when the confidence coefficient of the extrapolated pixel is lower than the basic confidence coefficient, enhancing the extrapolated pixel by using a segmented conservative estimation strategy; a correction image set and a corresponding confidence coefficient image set are obtained through iterative processing; carrying out weight distribution on the images in the corrected image set to obtain a corresponding normalized weight set; and performing multi-scale fusion according to the normalized weight set and the corrected image set to obtain a fused image. Pixel irradiance of an overexposure area is effectively reconstructed by performing pixel-level correction on an image, and high-fidelity fusion of Fourier space intensity is realized by performing multi-scale fusion on the image.

This invention provides a multi-exposure image fusion system, method, and electronic device. The system includes: an image sequence alignment and correction module, comprising a selection submodule and an image sequence alignment and correction network. The selection submodule is configured to select a reference exposure level image, and the image sequence alignment and correction network is configured to align and correct other exposure level images based on the reference exposure level image; an image fusion deep network, configured to fuse the aligned and corrected multi-exposure image sequence to generate a multi-exposure fused image; and a visual quality-driven perception optimization module, configured to evaluate the multi-exposure fused image using a fused image visual quality evaluation model and optimize the image fusion deep network based on the evaluation results. This invention, by performing multi-exposure image fusion based on a deep network, can effectively capture image details and better understand the content of multi-exposure images, thereby achieving fast and efficient multi-exposure image fusion.

This application proposes an image processing method, apparatus, and electronic device. The method includes: after acquiring an image frame sequence for the same shooting scene, fusing the first SDR image frame and the i-th SDR image frame in the image frame sequence by calling a dual exposure fusion engine to obtain a high dynamic range (HDR) image; then, obtaining an unfused SDR image from the image frame sequence as the SDR image to be fused, and fusing the unfused SDR image frame with the HDR image by calling the dual exposure fusion engine, and using the fused image as the HDR image; repeating the step of obtaining an unfused SDR image frame from the image frame sequence until an SDR image frame is obtained from the image frame sequence. Thus, without adding hardware, the dual exposure fusion engine enables the fusion processing of multiple image frames.

The application discloses a kind of multi-exposure fusion's angle resolution scattering micro-nano structure measurement method, it is related to integrated circuit measurement technical field.The method includes: obtaining multi-exposure sequence image set, camera response function CRF is modeled;Overexposed pixel value is estimated using linear extrapolation method based on tangent line at response curve end point, and the confidence of extrapolated pixel is calculated;When the confidence of extrapolated pixel is lower than basic confidence, segmented conservative estimation strategy is used to enhance to extrapolated pixel;Corrected image set and corresponding confidence image set are obtained by iterative processing;Weight distribution is carried out to the image in corrected image set, and corresponding normalized weight set is obtained;According to normalized weight set and corrected image set, multi-scale fusion is carried out, and fusion image is obtained.Pixel level correction is carried out to image, and the pixel irradiance of overexposure area is effectively reconstructed, and multi-scale fusion is carried out to image, and high-fidelity fusion of Fourier space intensity is realized.

The invention discloses an intelligent detection method and system for surface defects of a power battery, and belongs to the technical field of computer vision and industrial detection.The method comprises the steps that multiple images under the conditions of forward light, lateral light and diffused light are collected through sequential switching of an annular LED light source array; generating a high dynamic range composite image through multi-exposure fusion; performing multi-light-source feature fusion based on an attention mechanism; performing pixel-level semantic segmentation by adopting a U-Net + + network to output a defect mask and a confidence score; according to the method, the technical problems of image overexposure caused by high light reflection of the metal shell, inconsistent imaging contrast of different defect types, inaccurate defect boundary positioning and the like are solved, high-speed detection of 120 battery cells per minute is realized, and the defect detection rate reaches 99.5% or above.

The present application relates to the technical field of image processing, in particular to a high dynamic range image synthesis and tone mapping method and electronic equipment, the present application obtains multiple RAW images with different exposure under the same scene through a camera; the RAW images with different exposure are fused through a square weighting function to obtain a high dynamic range image synthesized in the RAW domain; the high dynamic range image is subjected to channel tone mapping to obtain a low dynamic range image in the RAW domain. The present application solves the problem of synthesizing and mapping a high dynamic range image on RGB, which occupies a large computer resource and seriously affects the color and brightness information of the image. The present application has fast calculation speed, low resource consumption, good result perception and is suitable for hardware integrated high dynamic range image synthesis, and has strong market application prospect.

The application discloses a quality evaluation method of a dynamic scene multi-exposure fusion light field image, which fully considers distortion characteristics and internal high-dimensional structure characteristics of a distorted multi-exposure fusion light field image from aspects of local, global and angle, etc., firstly performs tensor decomposition on an angle dimension of the distorted multi-exposure fusion light field image, which can effectively retain the high-dimensional characteristics and extract features more related to subjective perception on a tensor slice; then, a foreground part with significant artifacts is detected by designing a pre-background segmentation scheme before a first component, and edge structure and luminance / chrominance features of the foreground part are extracted; then, local features of low dark, normal and high bright areas are extracted based on an HSI model after luminance segmentation of the first component; subsequently, a perception feature vector is formed by combining global features and angle features extracted by other components, and an objective quality score is predicted by pooling, so that the predicted objective quality score has better consistency with visual perception of the human eye.

This invention discloses a real-time multi-exposure HDR image processing system based on FPGA, comprising: a DDR3 image storage control module, integrated within the FPGA, communicating with an external DDR3 chip via the AXI4 bus protocol, supporting high-speed storage and parallel reading of 8-channel multi-exposure images, and outputting synchronized multi-exposure image data and valid frame, line, and data signals; a multi-exposure HDR fusion module, connected to the output of the DDR3 image storage control module, employing a 6-stage hardware pipeline architecture to achieve pixel-level weighted fusion of 8-channel multi-exposure images, outputting a 24-bit RGB format HDR fusion image; and a Reinhard tone mapping module, connected to the output of the multi-exposure HDR fusion module, implementing dynamic range compression from HDR to LDR images based on an optimized Reinhard algorithm, outputting a 24-bit RGB format LDR image that meets the requirements of display devices. This invention achieves real-time processing of multi-exposure HDR images throughout the entire process through the integrated and synchronized optimization of these three modules.

The invention discloses a self-adaptive multi-exposure fusion method and system based on surface structured light three-dimensional measurement, and the method comprises the steps: firstly calculating a modulation degree graph and a maximum gray value based on an initial exposure image, dividing a plurality of regions according to the modulation degree, and carrying out the correction of the gray value of pixels and the weighting of local information entropy, so as to obtain a plurality of regions; in the image fusion stage, the average gray scale of each cluster is accurately calculated, then the optimal exposure time is solved for different regions, in the image fusion stage, an overexposure threshold is dynamically determined by analyzing the gray scale value distribution of a high-exposure image, the fusion weight is calculated by taking the pixel modulation degree under each exposure as the core basis, and finally a global high-quality fringe image is generated. According to the method, the problem of low reconstruction precision caused by local overexposure or underexposure of the high dynamic range object is solved, and the three-dimensional measurement precision and the automation level of the high dynamic range object are remarkably improved.

The invention discloses an automatic calibration method for multi-exposure fusion of engineering machinery. The automatic calibration method comprises a preparation stage and a calibration stage, in the calibration stage, the calibration accuracy is ensured through structural similarity evaluation; and performing multi-exposure fusion processing on the initial image with lost feature point information to ensure that a controller accurately identifies feature points, forming a corresponding mapping table through feature point matching, and quickly forming a complete panoramic image picture. According to the invention, the problem of illumination interference in an outdoor calibration environment is effectively solved, and the accuracy of calibration plate angular point identification is improved. The automatic processing flow reduces the manual intervention link and shortens the calibration operation time. The multi-exposure fusion technology ensures complete extraction of calibration plate features under different illumination conditions, the structural similarity evaluation mechanism ensures geometric accuracy of a panoramic image, and reliable panoramic visual support is provided for safe operation of engineering machinery.

The invention discloses a black lens chromatic aberration detection device and method based on hyperspectrum, and relates to the field of black lens chromatic aberration detection, and the device comprises a support frame, a hyperspectral camera, an annular light source, a laser range finder, a lens jig, a driving motor and a main control board. The method comprises the steps of initializing a system and collecting a dark background; fixing the lens and the low-signal-value standard white board; driving the jig to move and synchronously acquiring multi-exposure spectrum data and distance data; carrying out dark background deduction and distance light source compensation; synthesizing the multi-exposure data into high-quality fusion spectrum data through an intelligent fusion algorithm; calculating the relative reflectivity of the to-be-measured lens based on the fusion data and the known reflectivity of the standard white board; and finally converting the CIELAB chromatic value into a CIELAB chromatic value and outputting a result. Through multi-exposure fusion and distance compensation, the problems of weak signals, large noise interference, poor measurement repeatability and the like caused by low reflectivity of a black lens are effectively solved, and automatic detection of chromatic aberration parameters is realized.

The invention relates to the field of medical image processing, in particular to an examination image processing method based on multi-exposure fusion. Comprising the following steps: collecting multiple groups of images with different exposure intensities under the same inspection view angle; segmenting the throat key anatomical structure in each group of images to obtain initial boundary coordinates; generating a triangular mesh covering the key area in the range of the initial boundary coordinates as a feature unit; calculating a brightness characteristic value containing a brightness average value and a brightness gradient value for the characteristic unit; and establishing association of feature units among multiple images according to the brightness average value, selecting the clearest expression of each boundary based on the brightness gradient value, and finally fusing all the optimized boundaries to form an enhancement result. The detail expressive force of highlight and shadow areas in the image is remarkably improved, and the workload of multi-image comparison and analysis is effectively reduced while the diagnosis precision is ensured.

The invention discloses a signal lamp image generation method and device, electronic equipment and a storage medium. The method comprises the steps of obtaining a short-exposure image and a long-exposure image collected by monitoring equipment under the condition that the illumination intensity of an environment where the monitoring equipment is located is determined to be smaller than preset intensity; respectively determining a target short exposure fusion proportion of the short exposure image at the pixel point and a target long exposure fusion proportion of the long exposure image at the pixel point according to the pixel value of each RGB color channel of a plurality of pixel points of the region of interest in the short exposure image and the pixel value of each RGB color channel of the pixel point corresponding to the long exposure image; and fusing the short-exposure image and the long-exposure image according to the target short-exposure fusion proportion and the target long-exposure fusion proportion to generate a traffic signal lamp image. According to the scheme, the abnormal edge area of the signal lamp in the image can be effectively eliminated, the halo of the signal lamp is weakened, and the brightness from the signal lamp body to the halo area in the image can be smoothly and naturally transited.