44 results about "Gaussian pyramid" patented technology

The invention belongs to laser stripe image data processing, and particularly relates to a multi-scale gradient gravity center laser center line extraction method and system, and the method comprises the steps: S1, carrying out the feature extraction and enhancement of a laser stripe image, intensifying detail branches through a space channel weight map, obtaining a bounding box positioning result based on depth separable convolution and residual connection, and obtaining a bounding box positioning result; s2, acquiring a prediction center for each column by adopting a prediction method, and searching a gray peak value in a normal adaptive window to realize coarse positioning; the method comprises the following steps: S1, building a local coordinate system and an elliptical window along the coarse center line, and constructing a multi-scale Gaussian pyramid to carry out background suppression and image enhancement, S4, generating a weighted graph through cross-scale fusion, calculating a weighted gray gravity center in each window, and carrying out image enhancement on the weighted graph. And after smoothing, a high-precision laser stripe center line is output. And the accuracy and robustness of extracting the laser center line are improved.

The invention discloses an underwater image enhancement method and system based on three-input multi-scale fusion. The method comprises the following steps: carrying out visibility recovery on an image to be processed; performing contrast enhancement through a nonlinear mapping function; carrying out enhancement processing on the contour of the high response area; respectively calculating a Laplacian contrast weight map, a saliency weight map and a saturation weight map of the three processed images; linearly combining all types of weight maps to obtain an aggregation weight map, and normalizing the aggregation weight to obtain a normalized weight map; decomposing the three processed images into a Laplacian pyramid, decomposing the normalized weight map into a Gaussian pyramid, and performing fusion through upward sampling and addition reconstruction; carrying out smoothing processing and edge-preserving sharpening processing on the fused image to obtain an image after underwater image enhancement and restoration; according to the method, the color cast problem of the underwater image is solved, the image contrast is effectively improved, and the texture details of the image are improved.

The invention relates to a riverway ice condition identification method based on optics-SAR fusion and adaptive segmentation, and belongs to the technical field of remote sensing image processing and application. Riverway ice condition features are extracted from the optical remote sensing image, and a Ka-SAR feature map is extracted from the Ka-SAR image by adopting an improved high-resolution network; carrying out multi-modal and multi-scale feature fusion on the extracted features, carrying out scale specificity feature extraction by adopting a Gaussian pyramid, and then carrying out weighted fusion on the river ice condition features and the Ka-SAR features on each scale based on a scale specificity weight distribution principle; aggregating the multi-scale fusion features into a final fusion feature map by adopting a bottom-up pyramid reconstruction strategy; improved Kuan filtering is used to optimize the fused feature map, and adaptive threshold segmentation is used to realize ice surface and non-ice surface binary classification in the feature map. The method can achieve the precise segmentation of the ice condition region, and improves the recognition precision of the thin ice region.

This invention relates to an image denoising method, system, and readable storage medium. The method includes: performing Gaussian filtering and downsampling on a current frame image to obtain a first downsampled image; performing Gaussian pyramid decomposition to obtain a current frame image group; upsampling to obtain an upsampled image, and subtracting it from the current frame image to obtain high-frequency information; performing motion estimation and texture estimation at different scales on the current frame image group and a reference frame image group, and correcting them with guided filtering; performing spatiotemporal filtering on the first downsampled image and the reference frame image based on motion estimation weights; upsampling the denoised downsampled image to obtain a denoised upsampled image; and weighted fusing the denoised upsampled image and high-frequency information according to texture estimation weights to obtain a final image; downsampling the final image and performing Gaussian pyramid decomposition to obtain a reference frame image group for denoising the next frame image. This invention can preserve more texture details while increasing the image signal-to-noise ratio.

The application discloses an environment monitoring method and device, which is used for realizing accurate counting of river floating objects in a river monitoring image, so that a more accurate and real-time monitoring result of the river environment quality is obtained. The environment monitoring method provided by the application comprises the following steps: determining a river monitoring image; performing Gaussian filtering and smoothing processing on the river monitoring image, and performing down-sampling on the image obtained through the Gaussian filtering and smoothing processing for a preset number of times; constructing a Gaussian pyramid model by using the image obtained through the Gaussian filtering and smoothing processing and the image obtained through each down-sampling; the Gaussian pyramid model comprises multiple layers of images, wherein the bottom layer of image is the image obtained through the Gaussian filtering and smoothing processing, and each layer of image other than the bottom layer is the image obtained through one down-sampling; determining a region of interest of the river monitoring image by using each layer of image of the Gaussian pyramid model; counting the river floating objects in the region of interest; and determining the river environment quality based on the counting result.

This invention discloses a near-infrared image dehazing method based on bright area clustering optimization. The method is characterized by first acquiring a hazy near-infrared image, then constructing an image dehazing model and building a Gaussian pyramid for the hazy near-infrared image. Candidate bright area binary masks are extracted and refined at each scale, and after mapping and fusion, a baseline candidate bright area binary mask and scale persistence features are obtained. Next, feature extraction and cluster analysis are performed on the baseline candidate bright area binary mask, and a physical consistency cost function is constructed based on an atmospheric scattering model for verification, obtaining a global atmospheric light estimate. Then, the global atmospheric light estimate is used for normalization and adaptive dark channel extraction and fusion to obtain an initial transmittance distribution, which is then refined and constrained to obtain an optimized transmittance distribution. Finally, the dehazed image is reconstructed based on the atmospheric scattering model. The advantages are improved transmittance estimation accuracy and edge preservation capability, achieving high-quality restoration of hazy near-infrared images.

The invention relates to the technical field of medical image processing, and discloses a chronic obstructive pulmonary emphysema distribution quantitative method and system based on multi-scale segmentation, and the method comprises the steps: carrying out the anisotropic diffusion filtering noise reduction of a chest CT image; segmenting a lung field and removing a blood vessel bronchial structure by adopting a region growing algorithm; multi-scale image representation is constructed based on a Gaussian pyramid, an emphysema candidate area is identified in a coarse scale layer, and a boundary is accurately drawn by adopting a self-adaptive threshold value in a fine scale layer; extracting local texture features to distinguish the lobular central emphysema and the total lobular emphysema; dividing severity levels according to spatial aggregation characteristics and density gradient distribution, and calculating an air swelling volume ratio and a distribution heterogeneity index; the three-dimensional pseudo-color volume is used for drawing visualization, a structured quantitative report is generated, accurate segmentation and subtype classification of the emphysema area are achieved, and comprehensive quantitative analysis indexes are provided.

A method and device for decoding an encoded data is based on a hybrid implicit neural representation (INR) network comprising a plurality of INR networks arranged in a plurality of hierarchic levels and latent variables being arranged in a plurality of hierarchic levels. The ground truth images are decomposed using Gaussian pyramid to generate the output images at different resolutions, making it possible to learn one synthesis network per resolution for example. The reconstruction process may be performed in a coarse to fine manner, i.e. from the lowest to the highest resolution.

The application discloses a kind of video vibration two-dimensional modal identification method and equipment based on depth completion pyramid, belong to structural vibration measurement and modal identification technical field;First, the depth image sequence in the two-dimensional vibration process of measured structure is obtained;The sparse depth image of depth image sequence is directionally depth completed to construct Gaussian pyramid, and depth completion pyramid is constructed in combination with the Gaussian pyramid of adjacent scale, and the target layer is selected for fusion reconstruction;Then, the reconstructed depth sequence is sequentially subjected to difference amplification, transposition compression and direct current removal processing to construct a unified input matrix;Then, the input matrix is subjected to PDD method and H-DMD method to extract natural frequency, damping ratio and modal shape from two dimensions of frequency domain and time domain;Finally, the decomposition results of PDD method and H-DMD method are jointly checked, and the modal identification result of the structure is output.The two-dimensional structural vibration modal result obtained by the method is improved in terms of parameter integrity, spatial continuity and physical consistency.

The invention discloses an efficient real-time construction method and system for a large-range environment consistency three-dimensional map. According to the method, the global map deviation is controlled within a reasonable range through fusion pose estimation of the multi-source constraint improved DPVO and the wheel type odometer, a priori anchor point is used for assisting closed-loop triggering optimization, high-weight absolute anchor point constraint is set, a Gaussian pyramid is constructed in a self-adaptive manner, high-precision positioning is realized in a hazardous chemical substance warehouse, and the positioning accuracy is improved. The robot is effectively prevented from colliding with the goods shelf. According to the method, double-thread parallel mapping is adopted, so that the output frame rate of the local map meets the requirement, the conventional moving speed of the robot can be matched, and no lag of navigation is ensured. According to the invention, through multi-dimensional mask subgraph management and an adaptive pyramid, continuous inspection of the warehouse is realized, and full-process data support of'mapping-positioning-alarming-decision 'is provided for safety supervision of the hazardous chemical substance warehouse.

The application relates to an infrared and visible light image fusion method based on multi-scale decomposition and partial differential equations, and belongs to the technical field of image fusion. The method solves the problems of blurred target edges and details in a current fusion image and large resource consumption. The method comprises the following steps: obtaining an infrared image and a visible light image including a target object through synchronous acquisition, and carrying out pretreatment and adaptive multi-scale decomposition; the number of layers of a Gaussian pyramid is obtained based on the complexity of the visible light image and the infrared image; the pretreated visible light image and the infrared image are subjected to multi-scale decomposition by using the Gaussian pyramid corresponding to the number of layers; each decomposition image is subjected to smoothing filtering and detail enhancement to obtain an enhanced image of each scale; the enhanced images of the infrared and visible light of the same scale are subjected to dynamic fusion, and all the scale images obtained through the fusion are reconstructed to obtain a final fusion image. The method realizes the reservation of target edges and details in the fusion image by using small resources.

The application discloses a similar image search method, system and device based on perceptual hashing and a medium thereof. The method comprises constructing a multi-scale space for an input initial image and generating a Gaussian pyramid based on the multi-scale space, obtaining a Gaussian difference pyramid based on the Gaussian pyramid, screening stable key points in the Gaussian difference pyramid, delimiting a densest region in the initial image and intercepting a minimum circumscribed rectangle as a target screenshot image according to the distribution density of the screened stable key points, extracting multi-modal features of the initial image, performing feature splicing to generate a joint feature vector, preprocessing the initial image and the target screenshot image, generating corresponding hash strings based on the preprocessed images and the joint feature vector, comparing hash strings of images to be searched with a hash set in a database, and outputting images with a hamming distance less than or equal to a preset threshold as similar results. The application improves the search accuracy of screenshot images to obtain complete original image information.

Embodiments of the present application relate to a video saliency map generation method, device and storage medium. The method comprises: constructing a plurality of Gaussian pyramids containing different scales for each preset feature channel according to a plurality of preset feature channel information of a video frame; wherein the preset feature channels include brightness, color and edge; determining a first saliency map for each preset feature channel according to the Gaussian pyramids of a plurality of preset feature channels; obtaining simulated optical flow by calculating motion information under a preset dimension through inter-frame difference after different level image displacement according to brightness information of a plurality of video frames; extracting a second saliency map of a preset direction and a preset speed based on a center-edge difference method according to optical flow-speed information; and determining a video saliency map according to a static saliency map synthesized according to the first saliency map and a dynamic saliency map synthesized according to the second saliency map. The technical solution of the embodiments of the present application has fast saliency map calculation speed, small calculation overhead, good effect and strong interpretability.

The invention discloses a splicing algorithm dense feature point matching management tool based on GIMP, and the tool comprises a dense feature point detection matching algorithm: constructing a Gaussian pyramid for a selected matching image on a plurality of scales, and enabling each scale to correspond to a different resolution; acquiring a specified matching area of the matching image, and performing grid setting under different resolutions of the matching area; in each divided grid region, calculating the gradient intensity and direction of the image, and performing histogram statistics according to the gradient direction to form a direction gradient histogram; searching a local maximum value and a local minimum value on each direction gradient histogram as candidate key points; for each detected key point, gradient histograms are calculated in small windows around the key point, and a descriptor vector is generated. According to the method, the problem that a traditional feature point detection algorithm cannot detect the feature points by 100% due to the fact that the overlapping area between the images is too small in some splicing application scenes, and the condition of splicing failure is caused is solved.

The invention relates to the technical field of image analysis, and discloses a PCB (Printed Circuit Board) production defect detection method based on image analysis, which comprises the following steps: acquiring a surface image of a PCB, and carrying out image preprocessing on the surface image, including graying, noise filtering and image enhancement; multi-scale feature extraction is carried out on the preprocessed image to obtain feature maps under multiple scales, and the multi-scale feature extraction is based on Gaussian pyramid decomposition and local binary pattern texture feature fusion; and based on the feature map, performing registration comparison on the collected feature map and a feature map of a standard template. According to the method, through fusion of multi-scale image feature extraction and defect quantitative analysis, the recognition precision of PCB tiny defects and dense line anomalies is remarkably improved, the limitation that a traditional detection means is insufficient in subtle feature perception capacity is effectively overcome, and accurate mapping from the image level to defect features is achieved.

This invention belongs to the field of seismic data processing and relates to a method and system for consistent processing and adaptive fusion of seismic attributes in multiple contiguous seismic work areas. The method includes: S1: dividing the spatial coordinates of the original seismic attribute data into a grid and establishing a gridded data index; then, based on the gridded data index, executing a localized search strategy to extract the neighboring data point set of the seismic data points to be processed; S2: eliminating systematic value range differences in the original seismic attribute data based on the neighboring data point set and stitching them together to obtain consistent fused seismic attribute data; S3: using a hierarchical interpolation strategy to fill in blank areas to obtain continuous seismic attribute interpolation data; and S4: performing global optimization based on Gaussian pyramid decomposition and an energy minimization model to obtain the final fused seismic attribute data. This method achieves better work area stitching and interpolation, efficiently avoiding the attribute non-fusion effect of traditional work area stitching methods, thereby realizing the goal of consistent seismic attribute stitching across multiple work areas.

A multi-exposure dynamic range enhancement method for low-light images, for a group of low-light image sequences which are registered, continuous multi-exposure imaging of the same scene, for different exposure time original images, first, calculate the contrast, texture and exposure weight factors for each pixel, the three weighted multiplication and normalization to get the weight map corresponding to different exposure time; second, the original image of different exposure time is decomposed by Laplacian pyramid, and the weight map corresponding to different exposure time is decomposed by Gaussian pyramid; third, the Gaussian pyramid weight coefficient of different exposure time is weighted and averaged with the Laplacian pyramid layer by layer, and the new Laplacian pyramid is fused; finally, the fused Laplacian pyramid is reconstructed, and the final fused target scene high dynamic range low-light image is obtained.

This invention relates to the field of UAV pose map optimization technology, and particularly to a method for optimizing the pose map of a UAV monocular camera. The method includes: blurring different image frame data, constructing a Gaussian pyramid, and downsampling; determining candidate feature points by subtracting Gaussian blurred images of adjacent scales; accurately determining the positions of candidate feature points on the image frame; removing low-contrast candidate feature points and edge response feature points to obtain the final feature points; and using the LM algorithm to adjust the UAV attitude parameters and optimize the pose map for discontinuous image frame data. The process of Gaussian difference point interest detection on continuously acquired image frame data suppresses noise in the image frames, effectively smoothing noise in the image. The resulting pose map is smoother with smaller gradient changes, improving the robustness of attitude estimation. The pose map optimization process expands the scope of pose map optimization, allowing optimization even for discontinuous images.

The invention provides a live-action three-dimensional modeling method, medium and system based on stereo image pair satellite data, and belongs to the technical field of three-dimensional modeling. Geographic grid partitioning is carried out on a stereo image pair satellite image, and a Gaussian pyramid is constructed to extract multi-scale features; the features are input into a multi-scale matching fusion model fusing two dam water storage problem principles to obtain a corresponding point set with linear time complexity, resource scheduling of an image processing unit is optimized by using a double-layer game model, a dense parallax field is calculated based on the corresponding point set, and a three-dimensional point cloud is reconstructed; and finally, generating a three-dimensional grid model through registration fusion and a moving cube algorithm, performing texture mapping and illumination consistency processing on the three-dimensional grid model, and outputting a live-action three-dimensional model of the target area, thereby solving the technical problem that the matching efficiency of a large-range stereo image to satellite image features is low.

This invention belongs to the field of remote sensing image registration technology, specifically relating to a multimodal remote sensing image registration method based on multi-scale template matching. First, multimodal remote sensing image pairs are acquired, and a feature description map of each image is constructed. Multiple layer-by-layer downsampling operations are then performed on these maps to construct Gaussian pyramids for the two images. Next, feature points of the image pairs are extracted, and the Gaussian pyramids of the images to be registered are rotated in different directions. Based on the extracted feature points, a template matching algorithm is used to match the remote sensing image pairs in different rotation directions, selecting the direction with the most matching points as the principal direction. Finally, the spatial transformation relationship of the remote sensing image pairs is determined based on the feature matching results corresponding to the principal direction. This invention can reliably handle general multi-source remote sensing image registration problems, solving the problem that existing methods cannot effectively handle images with severe nonlinear radiation distortion, and exhibits strong robustness and reliability.

The invention belongs to the technical field of image processing, and particularly relates to an abnormal action detection method and system based on multi-scale optical flow feature fusion, and the method comprises the following steps: S1, obtaining a real-time video stream of a monitoring region, carrying out the graying processing of a continuous frame image, constructing a Gaussian pyramid comprising a fine scale layer and a coarse scale layer, and carrying out the gray processing of the continuous frame image; the optical flow module value of the fine scale layer and the optical flow module value of the coarse scale layer are respectively calculated; and S2, constructing a micro-motion saliency difference model by using the optical flow module value of the fine-scale layer and the optical flow module value of the coarse-scale layer, and introducing logarithmic gain by calculating the ratio relation of the two. The problem that the false alarm rate of an outdoor scene is high is fundamentally solved, meanwhile, by introducing weighting of the direction chaos degree, the system can accurately distinguish ordered operation and disordered trembling, and the blank in the field of weak abnormal sign monitoring is filled.

The application discloses a small data set craniomaxillofacial translation method based on GAN, which comprises the following steps: 1, collecting skull and facial CT image data; 2, performing image preprocessing, three-dimensional reconstruction and fairing treatment on the skull and facial CT image data to obtain complete three-dimensional models of the skull and the face; 3, placing the three-dimensional models of the skull and the face in the Frankfurt coordinate system to perform normalization operation; 4, performing vertical mapping of the three-dimensional models of the skull and the face on the XOZ plane in the Frankfurt coordinate system to obtain the front view images of the skull and the face; 5, introducing a Gaussian pyramid into a GAN network to construct a network model PCC-GAN for skull and facial translation; 6, training network parameters of the pyramid cycle consistency generative adversarial network model PCC-GAN; and 7, placing the skull and facial images into the craniomaxillofacial translation model PCC-GAN to generate two-dimensional skull and facial images, and more accurate and real facial images can be generated under the condition of less point cloud data.

The application discloses a large-amplitude motion estimation method, system, device and medium based on multi-scale phase video processing. The method is to down-sample the original image by using an image pyramid, reduce the image resolution of each scale layer by layer, use a Gabor wavelet as a filter for processing each scale image, and use the filter response amplitude as the confidence of phase estimation reliability; after constructing a phase-based motion constraint model, starting from the lowest resolution scale image, the least square fitting method is used to solve the optimal motion solution of the scale; the motion solution of the previous scale is used as the initial motion of the next scale, until the final motion solution is output. The significant effect of the application is that by constructing a layered phase unwrapping algorithm based on a Gaussian pyramid, the limitation of traditional amplitude is broken through, and high-precision tracking of large-amplitude motion is realized.

The invention discloses a micro-vibration monitoring method based on foreground detection and adaptive Euler video amplification, and belongs to the technical field of equipment micro-vibration monitoring, and the method comprises the steps: firstly collecting an electromechanical equipment operation video, extracting a foreground through a Gaussian mixture model to obtain a mask, and segmenting the mask into sub-regions through an SLIC algorithm; 4 vibration indexes of each sub-region are calculated and quantized intensity is fused, so that the number of layers of the pyramid and an amplification coefficient are matched; and building a Gaussian pyramid, a Laplacian pyramid and double IIR filtering to extract a target vibration signal, and reconstructing an image after amplifying and superposing an original signal according to parameters to obtain a vibration enhanced video. According to the micro-vibration monitoring method based on foreground detection and adaptive Euler video amplification, precise monitoring of micro-vibration of equipment is realized, adaptive matching of amplification parameters is realized, loss of details and artifacts are avoided, the identification degree of vibration signals is improved, meanwhile, the method adapts to a complex industrial environment, a clear vibration enhanced video is output, and the method is suitable for popularization and application. Faults of the assisting equipment can be found early, and operation and maintenance efficiency and safety are guaranteed.

The application relates to the field of computer vision, in particular to an image feature recognition method based on computer vision, which comprises the following steps: pre-processing an input image, removing image noise, correcting image gray deviation, and obtaining a pre-processed image; performing multi-scale feature extraction on the pre-processed image, acquiring image local texture features and global contour features under different scales, performing dynamic weight fusion, calculating dynamic fusion weights based on feature response values and local complexity factors, and performing dimension reduction processing on the fused features through a principal component analysis algorithm to obtain a target feature set; matching reference features, calculating the Euclidean distance between the target feature set and the reference features, determining an adaptive matching threshold, judging a matching result, and completing image feature recognition. Through adaptive filtering and gray equalization preprocessing, Gaussian pyramid multi-scale feature extraction, dynamic fusion and an adaptive matching threshold, the application realizes the improvement of feature extraction accuracy, representation ability and recognition robustness.

The invention discloses a submarine cable detection method based on a texture perception diffusion model. The submarine cable detection method comprises the following steps: preprocessing side-scan sonar image data; and inputting the preprocessed sonar image into a pre-constructed Gaussian pyramid. And performing quantitative co-occurrence analysis on the cable structure texture features of each scale to generate statistical texture features. And fusing the statistical texture features of each scale to form a texture feature tensor. And inputting the texture feature tensor as a condition into a double-order conditional diffusion model, and outputting a reconstructed sonar image of the submarine cable by the double-order conditional diffusion model. And inputting the reconstructed sonar image into a pre-constructed underwater acoustic propagation physical model, and outputting an optimized feature tensor. And on the basis of the optimized feature tensor, predicting a cable space position, a surface wear state and fault type probability distribution of the submarine cable. According to the technical scheme, sonar image detail enhancement and noise suppression are realized, and the accuracy and efficiency of submarine cable position identification, wear detection and fault evaluation are remarkably improved.

The invention discloses an image moire pattern filtering method and device. The image moire pattern filtering method comprises the following steps: step 1, acquiring an image containing moire patterns; 2, performing down-sampling on the basis of the image containing the moire patterns in the step 1 to construct a Gaussian pyramid, and performing block processing layer by layer; step 3, performing graying processing on the image to obtain a grayscale image, calculating a local mean value mu (x, y) and a local variance sigma 2 (x, y) to obtain a local standard deviation sigma (x, y), calculating an adaptive color space standard deviation lambda color and an adaptive space distance standard deviation lambda space by using the local standard deviation sigma (x, y), and performing adaptive bilateral filtering on the grayscale image layer by layer and block by block; and step 4, performing up-sampling processing on the image after the adaptive bilateral filtering processing in the step 3, and reconstructing the image upwards layer by layer to obtain an image without moire patterns. According to the image moire pattern filtering method, the operation of template replacement after the homologous moire pattern-free image is obtained is avoided, moire pattern filtering is directly carried out, and the method can be used for real-time detection.

The present application belongs to laser stripe image data processing, and particularly relates to a multi-scale gradient barycenter laser center line extraction method and system, which comprises the following steps: S1, feature extraction and enhancement are performed on the laser stripe image, details branches are strengthened through a spatial channel weight map, and a boundary box positioning result is obtained based on a depth separable convolution and a residual connection; S2, a prediction method is used to obtain a predicted center for each column, a gray peak value is searched in a normal self-adaptive window to realize coarse positioning; the robustness under the condition of fracture and noise interference is improved through confidence evaluation, and a coarse center line is formed after smoothing; S3, a local coordinate system and an elliptical window are established along the coarse center line, a multi-scale Gaussian pyramid is constructed for background suppression and image enhancement; S4, a weighted graph is generated through cross-scale fusion, and a weighted gray barycenter is calculated in each window, and a high-precision laser stripe center line is output after smoothing. The accuracy and robustness of the extracted laser center line are improved.

The application relates to an infrared and visible light image fusion method based on ResNet50 and a double pyramid, and belongs to the technical field of infrared and visible light image processing. A residual network ResNet50 is used as a feature extractor to deeply extract the features of a normalized source image in each residual block, generate an activity measure map, obtain a spatial frequency map of the normalized source image, perform point multiplication operation, generate a salient feature map of each residual block, generate a Gaussian pyramid weight map of each residual block, obtain a Laplace pyramid image on each decomposition layer, perform weighted average fusion to obtain a fusion image of each residual block, and finally obtain a final fusion image by using a pixel maximum algorithm. The application can better summarize the image fusion task, improve the precision of infrared and visible light image fusion, improve the brightness and contrast of the fusion image, effectively retain the detail information of the image, improve the quality and visual effect of the fusion image, and better meet the engineering application requirements.