77 results about "Exposure fusion" patented technology

The invention belongs to the field of computer vision, and particularly relates to an unmanned aerial vehicle aerial photography night image enhancement method based on long and short exposure fusion. Comprising the following steps: firstly, constructing a low-illumination synthetic data set; secondly, carrying out data preprocessing on the long-exposure image and the short-exposure image in the low-illumination synthesized data set to obtain a long-exposure image and a short-exposure image containing blurring and noise; then, a lightweight denoising and deblurring network is constructed, the first stage comprises a multi-scale input module, a dual-core feature fusion module and a feature reconstruction module, and the second stage adopts a channel attention mechanism module for fine tuning; designing a multi-scale perception loss function, and guiding network training; and finally, using the trained final model to enhance the to-be-tested unmanned aerial vehicle aerial night image. According to the method, the visual effect and fidelity of the aerial image of the low-illumination unmanned aerial vehicle are effectively improved by utilizing complementary information of continuous short exposure and long exposure.

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 an analysis and detection method and device based on a weighting function and multi-exposure fusion, and belongs to the field of immunochromatography analysis and detection. The analysis and detection method comprises the following steps: obtaining a multi-exposure image set under a plurality of multi-exposure time sequences, in the process of recovering a camera response function of an image acquisition system, using an HDR algorithm based on multi-exposure fusion, adjusting weights of different exposure images by adopting a segmented weight function, and accurately adjusting and controlling parameters of the segmented weight function. The characteristics of each concentration section in immunochromatography are reflected more finely, and an optimal weighting function is established to enhance the fitting accuracy of a camera response function, so that an immunochromatography HDR image with the highest quality is recovered, and the linear range of quantitative detection is expanded.

The invention provides a perception-consistent underwater image enhancement method, and relates to the technical field of image enhancement. The method comprises the following steps: firstly, performing dual-channel color compensation, white balance correction and histogram contrast enhancement on an original image to obtain a color enhanced image; secondly, performing median filtering and gamma correction on a brightness channel to generate a multi-exposure image sequence; extracting a detail layer and a base layer of each exposure image, performing weighted fusion based on contrast, exposure and saturation weights, and realizing detail enhancement in combination with rapid steering filtering; and finally, fusing the color enhanced image and the detail enhanced image according to the contrast ratio, the saturation degree and the structural saliency. According to the method, the image quality is improved through dual-path cooperation and dynamic exposure fusion, the method is suitable for scenes such as underwater monitoring, aquatic product monitoring and underwater inspection, complex training is not needed, and deployment on low-power-consumption equipment is facilitated.

The invention discloses a position-type-associated airplane power distribution equipment fastener wrong and neglected loading detection method, which comprises the following steps of: debugging a detection device, respectively acquiring a standard sample and a detected sample of airplane power distribution equipment after the debugging of the detection device is completed, and acquiring a template image, a sample image and a test image; exposure fusion is carried out on the to-be-detected image, forward noise addition and reverse noise reduction are carried out on the sample image, and diversified sample images are synthesized; the collected sample images and the synthesized diversified sample images jointly form a training image set to be used for training a built YOLO11-AEDSF model, and finally the trained YOLO11-AEDSF model is used for detecting the exposed and fused image to be detected, so that mistaken and omitted assembly defect recognition of the surface fastener of the aircraft power distribution equipment is completed.

The invention discloses a hub defect detection method based on a 2.5 D photometric stereo image. The method comprises the following steps: sending an acquired preprocessed hub image into a pre-trained deep learning model for identifying and positioning coating hub defects so as to output a detection result; wherein the preprocessing of the hub image comprises the following steps: acquiring images of a coated hub under various exposures through a camera, and fusing the images under the various exposures by adopting an exposure fusion algorithm to obtain the preprocessed hub image. The 2.5 D photometric stereo image technology is utilized, and supervised learning and unsupervised learning methods are combined, so that the detection of the defects of the high-reflective coating hubs with different colors is realized.

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 building engineering construction potential safety hazard tracking management system and method, and relates to the technical field of safety management. The three-dimensional sensing module is used for fusing information data sets and establishing a dynamic construction scene point cloud model; the posture analysis module is used for enhancing the image in the dynamic construction scene point cloud model, reconstructing the action of the shielded part in the enhanced dynamic construction scene point cloud model, and determining the illegal action; and the interactive verification module is used for comparing the violation action with a BIM construction progress model, generating a virtual safety protection layer when the violation action does not start a protection measure according to a plan, and sending the virtual safety protection layer to an AR inspection terminal. According to the invention, the three-dimensional structure change of the construction site can be captured in real time, the problem of image blurring caused by poor illumination conditions or air pollution is effectively improved by using dynamic multi-exposure fusion and concentration grading processing technologies, and the image recognition precision is improved.

The invention belongs to the technical field of image processing, and particularly relates to a wide dynamic image fusion method and device for a fast moving scene. A single-lens double-light-path light splitting method is adopted, optical systems with the same type of sensors, the same exposure duration and different aperture coefficients are adopted for double light paths, field angles are matched, scenes are the same, and the error is smaller than 0.5 pixel; the method comprises the following steps: performing video image acquisition, performing wavelet transform on acquired two paths of image data, decomposing two paths of image matrixes to obtain a high-frequency detail image and a low-frequency-width dynamic image, and performing multi-depth-of-field fusion on the high-frequency detail image; performing multi-exposure fusion on the low-frequency wide dynamic image to generate HDR wide dynamic data, and performing tone mapping to form an LDR low-frequency image; and combining the high-frequency fusion detail image and the LDR low-frequency image. According to the method, the image without overexposure, with large depth of field and multiple details can be obtained, and the effects of video processing tasks such as target searching and tracking are effectively improved.

The invention discloses a multi-exposure fusion HDR image reconstruction method based on a flexible lens, and the method comprises the steps: carrying out the image reconstruction of an LDR image sequence through a trained exposure image reconstruction model, and obtaining an HDR reconstruction image; the exposure image reconstruction model comprises a pseudo twin space-frequency domain attention module, an exposure guide fusion module and an exposure recovery module; after features of an input sequence are extracted through an encoder, feature modulation is carried out through a pseudo twin space-frequency domain attention module, weighted fusion is carried out on a modulated feature map through an exposure guide fusion module, illumination recovery is carried out through an exposure recovery module, shallow features of a reference image are fused, and finally an HDR reconstruction image is generated through a decoder. According to the method, the limitation of a traditional model on the fixed number of lenses is broken through, self-adaptive processing on the input sequence of any length is realized, and meanwhile, the detail retention and artifact suppression capabilities are remarkably improved through structural tensor loss.

The invention provides a three-dimensional two-dimensional code surface information reconstruction method, device and equipment and a storage medium, and belongs to the technical field of image processing and three-dimensional reconstruction, and the method comprises the steps: carrying out the contrast enhancement of an initial image set of a three-dimensional two-dimensional code based on an exposure fusion contrast enhancement algorithm, and obtaining a contrast-enhanced image set, the initial image set comprises three-dimensional two-dimensional code images obtained by lighting and shooting the three-dimensional two-dimensional code at four different space angles; normal vector field reconstruction is carried out on the surface of the stereo two-dimensional code based on a four-light-source photometric stereo method and a contrast enhancement image set; converting the established normal vector field into a depth image based on a Poisson solution; and obtaining a reconstructed two-dimensional code image based on the depth image. According to the invention, the technical problem that the error of the surface information of the low-contrast stereo two-dimensional code extracted in a complex environment in the prior art is large is effectively solved.

The invention 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 overexposure image and an underexposure image, respectively obtaining dictionary features and sparse features of the overexposure image and dictionary features and sparse features of the underexposure image, and respectively updating the dictionary features and sparse features of the underexposure image and the overexposure image through a total objective function, common features are extracted from the updated dictionary features and sparse features through a nonlinear function, feature extraction is carried out according to the extracted common features, an overexposure fusion image and an underexposure fusion image are obtained and fused, a reconstructed image is obtained and compared with a standard image, and an evaluation index is generated. Transformation learning is carried out through total objective function iteration change, sparse features with higher quality are extracted, important internal attributes of the image are captured to reconstruct the image, and fusion of the multi-modal image is achieved.

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.

A multi-exposure image fusion method belongs to the field of image processing. The method includes the following steps: S1: Calculate the average brightness of each input image and sort them in ascending order of average brightness; S2: Perform intensity mapping on the middle image so that its average brightness is at the middle gray value; for the remaining images, perform intensity mapping according to their ranking numbers so that the input image sequence contains images with various exposure levels; S3: Use the middle image as a reference image, divide each image into blocks, perform dynamic image detection and replacement block by block. S4: Perform multi-exposure fusion on the image sequence that has undergone dynamic detection and replacement to obtain the final fused image. The input image set of this method contains images with various exposure levels, improving the fusion effect; and the pre-dynamic image detection can screen out the moving objects in each image, replace them and perform the final fusion, improving the dynamic range of the image and avoiding the ghosting caused by moving objects.

The application provides a multi-exposure diffraction image high dynamic range fusion method and system based on a Transformer, and relates to the fields of image processing and artificial intelligence reconstruction. The method comprises the following steps: obtaining multiple diffraction intensity images of the same sample formed under multiple different exposure times, and performing preprocessing to obtain preprocessed diffraction images; generating exposure perception information according to the preprocessed diffraction images; extracting multi-exposure diffraction image features based on the exposure perception information; performing cross-exposure Transformer fusion on the multi-exposure diffraction image features to obtain initial fusion features; performing radial frequency domain prior constraint on the initial fusion features to obtain high dynamic range fusion features; generating a high dynamic range diffraction intensity image based on the high dynamic range fusion features; and performing phase recovery according to the high dynamic range diffraction intensity image to obtain a sample spatial structure reconstruction result. The technical problem of insufficient effective information retention capability in the multi-exposure diffraction image fusion process of the prior art is solved.

The invention provides an imaging method and system in a strong light environment based on multi-exposure fusion, and relates to the technical field of image processing, and the method comprises the steps: obtaining K RGB images of a target region, and converting the K RGB images from an RGB space to an HSV space, thereby obtaining K HSV images; extracting V channels of the K HSV images to obtain K V channel images; obtaining K weight graphs based on the K HSV images; constructing K weight Gaussian pyramids based on the K weight graphs, further constructing K V-channel Gaussian pyramids, and constructing K Laplacian pyramids based on the K V-channel Gaussian pyramids; obtaining a fused Laplacian pyramid based on the K weight Gaussian pyramids and the K Laplacian pyramids; constructing a fused image based on the fused Laplacian pyramid; and converting the fused image from the HSV space to the RGB space to obtain a final imaging result. According to the method, the calculation amount is reduced, and the fusion efficiency is greatly improved.

The present invention discloses a multi-exposure image fusion method based on pyramid convolution, comprising the following steps: Step 1: splicing overexposed and underexposed images by channel and preprocessing them to obtain a system input; Step 2: sending the system input to a decorrelation module to eliminate channel-level and pixel-level correlations in the data; Step 3: performing feature fusion on the output of Step 2 through an encoder and mapping it to a high-dimensional data space; Step 4: performing dimensionality reduction on the fused high-dimensional data through a decoder, reconstructing the fused image, and completing multi-exposure fusion. The multi-exposure image fusion method of the present invention can improve halos and artifacts when fusing extreme lighting scenes, enhance the generalization ability of the multi-exposure image fusion algorithm for extreme scenes, and is widely used in scenes such as underwater and nighttime observation.

The present invention discloses a multi-exposure image fusion method based on a multi-dimensional collaborative refinement network, including: constructing a collaborative extraction module CEM to obtain shallow features and deep features of an image; obtaining an attention feature map through a self-attention module SAM; correcting the extracted edge texture of image features using an edge correction module ERM; and using a combination of two loss functions to train an image fusion network, wherein the image fusion network includes a collaborative extraction module CEM, a self-attention module SAM, and an edge correction module ERM. In order to improve the quality of the fused image and avoid artifacts, the present application designs a collaborative extraction module to extract and optimize image features. With the cooperation of the self-attention module, the texture, brightness, and color of the multi-exposure fused image are more prominent; the edge correction module complements CEM and SAM, and it can reduce the impact of noise on features at different scales, enhance the texture details of the feature map, and ensure the edge effect of the fusion result.

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 invention discloses a method for intelligently detecting mistaken and neglected installation of fasteners of an airplane power distribution system under complex illumination, and the method comprises the steps: installing a tested piece on a precise rotary table, and forming an automatic image collection platform through an industrial camera and an annular LED light source which are disposed on an automatic guide rail; and then a high-quality fusion reconstruction image is obtained by using a multi-exposure fusion technology, and misloading and neglected loading positions of the fasteners are automatically detected through a detection model with a deep learning algorithm, so that comprehensive, efficient and accurate detection of the positions of all the fasteners on the surface of the power distribution unit is realized, and the safety and reliability of an aircraft power distribution system are ensured.

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 invention relates to a method for generating a keratosclera feature map based on a Scheimpflug image. The method comprises the following steps: step 1, acquiring data of a Scheimpflug cross-section image and a front image; 2, extracting the edge of the keratosclera tissue; step 3, correcting image distortion; 4, reconstructing three-dimensional data of the keratosclera; 5, correcting coordinate axes of the three-dimensional data of the keratosclera; step 6, analyzing and expressing the three-dimensional data of the keratosclera; step 7, generating a keratosclera rise topographic map; 8, calculating the characteristics of the keratosclera data; and 9, generating a relative height topographic map of the keratosclera. The method solves the problems of single measurement angle, large error and few directions in the technology of measuring the three-dimensional geometrical morphology data of the single keratosclera before wearing the selected film, and overcomes the defect of high exposure of a Scheimpflug image in a sclera area through a high-low double exposure technology; establishing a keratosclera three-dimensional data structure through an exposure fusion technology and an image segmentation technology; and quantizing the optimal fitting spherical surface of the cornea and the optimal fitting spherical surface of the sclera to generate a rise and height topographic map.

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 present invention discloses a tracking and management system and method for potential safety hazards in construction engineering, which relates to the technical field of safety management. It includes: a three-dimensional perception module: fusing the information data set to establish a point cloud model of the dynamic construction scene; a posture analysis module: enhancing the images in the point cloud model of the dynamic construction scene, and at the same time reconstructing the actions of the occluded parts in the enhanced point cloud model of the dynamic construction scene to determine the violation actions; an interactive verification module: comparing the violation actions with the BIM construction progress model, and when the violation actions do not start the protection measures as planned, generating a virtual safety protection layer and sending it to the AR patrol terminal. The present invention can capture the three-dimensional structure changes on the construction site in real time, and at the same time uses dynamic multi-exposure fusion and concentration grading processing technologies to effectively improve the image blurring problem caused by poor lighting conditions or air pollution, and improve the image recognition accuracy.

Disclosed is a server apparatus for image processing, the server apparatus comprising an input module, the input module configured to obtain input image data, the input image data comprising a plurality of differently exposed images; an image processing module configured to process the plurality of differently exposed images to generate a composite image; wherein the image processing module comprises a neural augmented multi-scale exposure fusion function, the neural augmented multi-scale exposure fusion function comprises: a plurality of levels; one or more weight maps of the plurality of differently exposed images, each weight in the one or more weight maps associated with an image of the plurality of differently exposed images; a plurality of first pyramids corresponding to the plurality of levels, the plurality of first pyramids generated based on the one or more weight maps; and a plurality of hybrid pyramids, each of the plurality of hybrid pyramids constructed based on at least one edge- preserving smoothing function using the plurality of first pyramids as input.

The present invention designs a leaf reflective coding image stripe repair system and method combined with multi-exposure fusion. The repair system includes: an image acquisition module, an image fusion module, a mask image production module, an image annotation module, a generator module and a discriminator module; the image acquisition module collects leaf stripe images of different exposures, and the image fusion module fuses leaf stripe images of different exposures under the same posture to obtain an exposure-fused leaf stripe image; the mask image production module produces an irregular mask image; the image annotation module annotates the reflection and stripe confusion and missing parts in the leaf stripe image to obtain an annotated leaf stripe image, namely a label image; the generator module uses the leaf stripe image fused by the multi-exposure algorithm and the irregular mask image as input to perform feature extraction and reconstruction to generate a finely repaired leaf stripe image; the discriminator module distinguishes the leaf stripe image generated by the generator from the label image.

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.