75 results about "Reflectance map" patented technology

The invention discloses a weak light image enhancement method fusing noise adaptive diffusion and illumination perception, and the method comprises the steps: firstly designing a reflection-illumination decomposition module RID based on a Retinex theory, and extracting a reflectivity component and an illumination component of an input weak light image; then designing an ADRD (adaptive diffusion reflection denoising) module, performing denoising enhancement on the reflectivity component, and outputting an enhanced reflectivity image; introducing a noise estimation network, and dynamically adjusting the de-noising amplitude of the diffusion model according to the noise intensity of the region in the reflectivity graph; designing an illumination perception enhancement module IEM, fusing an attention mechanism and a local enhancement block, enhancing the illumination image, and outputting an enhanced illumination component; and building a complete neural network framework, carrying out joint training on the provided module, finally testing the stored neural network model, and outputting an enhanced image. According to the method, the problems of detail loss, noise residue and uneven illumination of the enhanced image in the prior art are solved.

A visual defect detection method, device, medium and product relate to the field of visual detection, and the method comprises the following steps: firstly, recording brightness changes of pixel points in different illumination directions by constructing an illumination response matrix, and establishing a complete mapping of surface reflection characteristics; secondly, performing joint calculation by using the light source direction matrix and the illumination response matrix to obtain normal vector data and reflectivity data, and representing spatial geometric features and material optical features of the surface respectively; thirdly, calculating a gradient feature map, a curvature feature map and a deformation feature map based on the normal vector data, generating a reflectivity map by using the reflectivity data, and forming a multi-dimensional feature representation system; and finally, in a defect detection stage, a strategy of combining gradient feature pre-screening with multi-feature verification is adopted, and final judgment is carried out by calculating a comprehensive defect feature value, so that the technical problem of insufficient micro defect detection precision of a visual detection method in the prior art is solved.

The invention discloses a method for clearly imaging a mask under an optical objective, and relates to the technical field of optical detection and image processing, and the method comprises the following steps: S1, constructing a light intensity distribution model based on the boundary condition of the field of view of the optical objective in combination with the reflection characteristic and incident angle change rule of a metal edge material, and determining the coverage range of metal edge reflection crosstalk, generating a mask boundary interference prediction map; s2, according to the mask boundary interference prediction map, performing region division on the micro-reflectivity image, extracting brightness gradient characteristics of reflectivity lifting in an interference region, and generating a brightness gradient parameter set required by image filtering; the method is based on light intensity modeling, fusion direction filtering, pixel stripping, gradient reconstruction and credibility weighted fusion, realizes closed-loop control from interference prediction to pixel restoration, has high resolution and adaptivity, can accurately strip edge reflection artifacts and restore a real image structure, remarkably reduces misjudgment and rework rate, and is suitable for large-scale popularization and application. And the mask yield and the stability of the detection system are improved.

The invention discloses a multi-source data fusion image sequence generation method and system based on space-time diffusion, and belongs to the technical field of image sequence generation, and the method comprises the steps: taking an existing satellite remote sensing image and an existing radar echo reflectivity image as input, taking an existing lightning drop point space distribution diagram as output, and training a synchronous mapping network; training a prediction network through the existing satellite remote sensing image and the existing radar echo reflectivity image to obtain a future satellite remote sensing image and a future radar echo reflectivity image; inputting a future satellite remote sensing image and a future radar echo reflectivity image into the trained synchronous mapping network to obtain a first prediction map of a future lightning drop point spatial distribution map; inputting the existing lightning drop point spatial distribution map into the space-time diffusion model to obtain a second prediction map of the future lightning drop point spatial distribution map; and the first prediction image and the second prediction image are spliced to obtain a final prediction image sequence of the future lightning drop point spatial distribution map, so that the prediction precision and robustness are remarkably improved.

The invention discloses a high-speed camera for semiconductor graph-free dark field detection and a detection method, and relates to the technical field of semiconductor detection, and the method comprises the steps: integrating an adjustable laser light source array, a high-performance TDI camera and a pre-scanning sensor, carrying out the brightness information collection of a pre-scanning region, and generating a pre-scanning reflectivity graph; sequentially irradiating a pre-scanning area with different illumination light through an adjustable laser light source array, generating a plurality of scanning images, and extracting features to generate a multi-modal differential feature field; predicting a main defect type in the region through a multi-mode illumination and defect feature collaborative enhancement algorithm to generate a corresponding illumination mode instruction; calculating an optimal TDI parameter through a TDI parameter dynamic modulation algorithm to generate a TDI camera control instruction; and scanning the wafer according to the two instructions, collecting original image data, inputting the original image data into the depth defect classification model, and outputting a defect information graph and corresponding information. The invention aims to solve the problem that semiconductor detection precision and efficiency are difficult to balance.

The invention belongs to the technical field of image processing, and particularly relates to a motor assembly defect detection system based on image recognition, which comprises a highlight area segmentation module, a multi-scale highlight repair module, a reflectivity decomposition module and a defect detection module, and is characterized in that a stator magnetic steel potting image is acquired, a highlight area is segmented, and a highlight mask is obtained; constructing a multi-scale pyramid based on the brightness component and the saturation component of the stator magnetic steel potting image, and generating a mixed weight map according to the highlight intensity and the detail significance under each scale; according to the multi-scale pyramid, the mixed weight map and the highlight mask, carrying out layered restoration on the brightness component to obtain a synthetic substrate image, and according to the synthetic substrate image, carrying out guided filtering and reflectivity decomposition to obtain an inherent reflectivity map; and carrying out encapsulation defect detection according to the inherent reflectivity graph. According to the invention, the interference of highlight reflection on encapsulation defect detection is eliminated, and the accuracy and robustness of encapsulation defect detection are improved.

The invention relates to the technical field of defect detection, in particular to a laser remelting surface defect area segmentation method which comprises the following steps: S1, acquiring a laser remelting surface digital image; s2, performing illumination reflection separation processing on the surface digital image to obtain a reflectivity image, and performing highlight detection and suppression on the reflectivity image to obtain a highlight suppression image; s3, on the highlight suppression image, estimating the surface dominant texture orientation and consistency of the highlight suppression image based on neighborhood gradient statistics; by processing the digital image of the laser remelting surface, the image quality is improved from the source, background texture interference is specifically eliminated, fine defect features are intelligently enhanced, and finally high-precision and robust segmentation of the laser remelting surface defects is realized through global optimization and refined post-processing. The problems of low detection precision and poor reliability caused by surface characteristics in the prior art are effectively solved.

The invention discloses a hydraulic concrete structure underwater surface crack detection method, and relates to the technical field of image sensors, and the method comprises the steps: carrying out the transmissivity mapping and background radiation field separation of underwater light field characterization parameters, generating a target reflectivity image, carrying out the multispectral-polarization data fusion of the target reflectivity image, and generating an enhanced feature image; performing multi-scale convolution kernel space transformation and feature pyramid fusion on the enhanced feature image to generate a crack response diagram, and performing anisotropic non-maximum suppression and morphological skeleton refinement on the crack response diagram to generate crack detection data; and performing geometric topology joint optimization and parameter quantification on the crack detection data, and outputting a structured detection report. According to the invention, by constructing the light field transmission model and synchronously carrying out polarization and depolarization ratio combined noise filtering and geometric distortion correction, adaptive noise filtering is realized, and the measurement precision is improved.

The invention discloses a terminal welding abnormal defect detection method, and relates to the technical field of image data processing, and the method comprises the following steps: carrying out the image collection through a multi-light-source image collection module, and obtaining a single-light-source image and a uniform-light-source image; calculating through a single light source image to obtain a reflectivity image, and obtaining a reflection grey-scale map through bilateral filtering and nonlinear transformation of an enhanced kernel function; fourier transform is carried out on the reflection grey-scale map, and a frequency domain interest map is obtained through mean filtering and subtraction; performing feature extraction through binary matrix sampling and a conditional probability model to obtain a two-dimensional interest graph; carrying out two times of pixel point screening and maximum value suppression on the reflection grey-scale map to obtain a texture interest map; carrying out HSV color space conversion on the uniform light source image, and calculating through Gaussian filtering and a normal form distance model to obtain a color interest graph; and obtaining a defect feature map through weighted fusion, carrying out threshold segmentation on the defect feature map, marking defects, and completing identification of the terminal welding defects.

Embodiments herein provide a method and system for a vicarious calibration of optical data from satellite sensors for urban scene flat fields. Identifying test sites automatically in the urban scene helps in vicarious calibration or on-board calibration of the hyperspectral / multispectral image. An internal average relative reflectance is calculated to get a relative reflectance of the image. Band ratios for various pixels is determined to assess flatness of the spectrum. Flat field candidates are identified from the various pixels having average band ratio nearing zero and a morphological technique is applied to determine a flat field. Finally, the image is calibrated vicariously based on the determined flat field as a test site. The on-board calibration of the remote sensing image may lead to a faster way to get the reflectance image of the scene, with the help of the calibration constants.

The invention discloses an image fusion method and system with low light enhancement and overexposure suppression, and belongs to the technical field of image processing, and the method comprises the steps: extracting a reflectivity feature map, a visible light feature map and a dark channel feature map of a visible light image and an infrared feature map of an infrared image in the training process of an image fusion model; obtaining an enhanced reflectivity image according to the reflectivity characteristic pattern; fusing the reflectivity feature map, the visible light feature map and the infrared feature map, and then performing color space conversion to obtain a color fused image; determining an overexposure area and a non-overexposure area of the dark channel feature map; replacing the overexposure area with the features in the infrared feature map, and replacing the non-overexposure area with the features in the enhanced reflectivity image to obtain an overexposure suppressed fusion image; and the fusion image subjected to overexposure suppression participates in model loss calculation. The quality of the color fusion image is improved, and the technical problem that the image fusion quality is affected by overexposure at present is solved.

The present application relates to a kind of multi-surface reflection error correction method and equipment for pressure sensitive paint measurement, introduce the process of "pressure sensitive paint luminescence to camera imaging" is simulated by micro light ray tracing technique, based on the known pressure sensitive paint reflectivity, image resolution, camera and model relative position such as parameter, accurate analysis model surface pressure sensitive paint real luminous intensity by back propagation algorithm.Finally, by adjusting the reflection parameter of pressure sensitive paint to zero, directly generate the image without reflection influence under the view angle of camera imaging, so that the corresponding relationship between image pixel and model surface is maintained unchanged while effectively eliminating the interference of multi-surface reflection, avoid additional image registration step.Compared with prior art, the present application has the advantages of high measurement accuracy, wide application scenarios, low cost and the like.

A precipitation scanning aperture RADAR system comprises at least one transmitting satellite (TX) and a plurality of receiving satellites (RXs) flying in a multi-static formation. The TX emits fixed frequency, pseudo-random, non-repeating phase modulated RADAR energy. Range and Doppler information is extracted from reflected signals received by the RXs to form coherent 2D along track / vertical images, which are combined into a 3D reflectivity map using a power-based incoherent modeling algorithm, such as a Gaussian mixture model. Embodiments implement a spotlight mode in which, for each RX, a plurality of the coherent 2D images obtained during successive coherent processing intervals (CPIs) are incoherently combined, and the satellites adjust their orientations between each CPI to compensate for satellite movement. Inter-satellite timing synchronization of 10 ns or better is sufficient for forming the 3D reflectivity map. The range and Doppler information can be extracted by concurrently demodulating at a plurality of reference frequencies.

Disclosed are a monocular high-resolution multispectral imaging system and a control method, relating to the technical field of vegetation index monitoring. The imaging system includes: an image sensor, configured to convert an optical signal of each waveband into a digital signal; a distortionless imaging lens, configured to focus an original image optical signal of a target object on the image sensor to generate a red-green-blue (RGB) original image; a multi-waveband combined narrow-band optical filter, placed between the distortionless imaging lens and the image sensor and configured to constrain a spectral range of an actual receivable optical signal of the image sensor; an incident light calibration module, configured to acquire incident irradiance of ambient light; and a parallel data processing unit, connected to the image sensor and configured to analyze the digital signal in combination with the incident irradiance and generate a reflectivity image for vegetation analysis. It is conducive to solving the problems of high cost as well as insufficient image synchronization and registration precision of existing multispectral sensors.

The present invention relates to a self-attention multi-scale encoding and decoding intrinsic image decomposition network, method and application, and belongs to the field of image recognition technology. The network uses a parallel encoding and decoding strategy, that is, two sets of identical but independent encoders and decoders are used to generate images of reflectance maps and illumination maps. Each network includes three parts: an encoder, a decoder and a skip connection. The encoder and decoder are both composed of multiple self-attention modules and downsampling or upsampling modules. The adaptive window self-attention proposed in this method can adaptively adjust the window according to the image content to strengthen the network's attention to different types of features and reduce the resource usage of the model; the channel enhancement self-attention module enhances the effective information in the feature map by expanding the self-attention calculation between different dimensions.

The invention discloses a defect identification method and device based on multi-modal data of a chip preparation process, and the method comprises the steps: obtaining a graphite carrier image after epitaxial growth is completed, and generating a graphite carrier and wafer mapping graph; the method comprises the following steps of: performing multi-modal imaging on a wafer to be detected, acquiring a gray scale image, a reflectivity image and a photoluminescence intensity image, synchronizing according to a pixel level, respectively performing normalization processing, and calculating a physical normalization factor according to a normalization result to obtain a multi-modal image; constructing a wafer grid space database, and storing abnormal areas in the multi-modal image, the electrical test data and the graphite carrier image into the wafer grid space database; and based on the mapped multi-modal image, the electrical test data and the multi-source data of the abnormal region in the graphite carrier image, identifying the defect type according to a preset rule. The problem that in the prior art, due to the fact that data generated in the chip manufacturing process are mutually independent, the defect root cause analysis efficiency is low is solved.

A method for performing point-to-point whiteboard parameter ratio correction on a hyperspectral image includes: capturing hyperspectral data of a standard reference whiteboard in advance as white(x,y,w), and storing the records, then captures hyperspectral data of a sample as sample(x,y,w); then selecting an unobstructed and unshaded whiteboard area within a certain range of the hyperspectral data sample(x,y,w) of the test sample, and labeling the area as Area A; calculating a spectral average of the ROI on the sample image as SA(w); calculating a spectral average of whiteboard data in the same position as the ROI as WA(w); dividing the two spectral averages, and obtaining a correction coefficient alpha(w)=WA(w). / SA(w); multiplying the whiteboard parameter ratio correction coefficient alpha(w) by a sample reflectance image matrix after whiteboard parameter ratio correction to obtain a final hyperspectral reflectance image matrix REFL(x,y,w)=alpha(w).*sample(x,y,w). / white(x,y,w).

The invention relates to the technical field of image detection, and discloses a floor processing quality detection method and system based on machine vision. The method comprises the following steps: collecting multiple frames of images of the surface of a floor through multi-angle structured light illumination, constructing a pixel-level luminosity response vector, and decomposing the pixel-level luminosity response vector into a diffuse reflectance graph, a specular reflectance graph and a normal vector graph based on a physical illumination model; and respectively detecting material flaws, coating unevenness and geometric scratches and pits in each component graph by adopting a targeted algorithm, and finally fusing results to generate a high-precision detection report. The system comprises a high-speed camera and a dome-type multi-light-source module which are synchronously controlled, a data processing unit and a defect decision fusion module. According to the method, interference suppression is realized through physical attribute decoupling, and the accuracy and robustness of defect identification are remarkably improved.

The invention is suitable for the technical field of color management, and provides an inkjet color calibration method and system based on material characteristics, and the method comprises the steps: obtaining a multiband reflectivity image of the surface of a to-be-inkjet substrate through multispectral imaging; extracting material features of the base material from the reflectivity image; acquiring ambient light spectrum data, and correcting the color appearance model; based on the material features and the corrected color appearance model, mapping a target color space to a device-related color space through a color conversion model, and generating a color conversion lookup table; according to the material characteristics, ink jet parameters are optimized; and according to the color conversion lookup table and the optimized inkjet parameters, controlling inkjet equipment to execute inkjet. According to the invention, the technical problems of color distortion, low calibration efficiency and inconsistent colors of multiple devices during cross-material painting due to the fact that the optical property difference of materials is not considered in the prior art are solved.

The invention is suitable for the technical field of equipment control, and provides an equipment control method, equipment and a computer program product. The equipment control method is applied to target equipment provided with a three-dimensional distance sensor and a shooting device, and comprises the following steps: acquiring a depth image and a reflectivity image of an environment where the target equipment is located through the three-dimensional distance sensor; obtaining a texture image of an environment where the target equipment is located through a shooting device; according to the depth image, the reflectivity image and the texture image, determining the object type of a target obstacle in the environment where the target equipment is located; and controlling the target equipment to move according to the object type. According to the equipment control method, the omnibearing information of the target obstacle can be fully acquired according to the depth image, the reflectivity image and the texture image of the environment where the target equipment is located, and then the target equipment is controlled to move, so that the obstacle avoidance accuracy of the target equipment is improved.

The invention discloses a thin cloud atmosphere top reflectivity estimation method and system based on deep learning. The method comprises the following steps: firstly, performing radiometric calibration on obtained remote sensing data to obtain a reflectivity image; secondly, 1 / 2 of a wave band is input, end-to-end feature extraction and cloud TOA reflectivity inversion are achieved through a convolutional neural network, and a preliminary inversion result is output; and finally, sampling and generating a denoised cloud TOA reflectivity image by using the trained denoising diffusion model according to the preliminary inversion result. According to the method, thin cloud reflectivity estimation is modeled as an image recovery problem, and preliminary inversion of thin cloud radiation characteristics is realized through an empirical relationship between wavebands. The advantages of the diffusion model in the aspects of image feature expression and reconstruction are combined, the radiation contribution of the cloud is accurately separated from complex earth surface and atmosphere mixed signals, effective estimation of the TOA reflectivity of thin cloud areas of different types and concentrations is achieved, and therefore a more robust physical basis is provided for downstream cloud detection and cloudless image recovery.

The application discloses a satellite remote sensing data collection and preprocessing method and system, and relates to the technical field of satellite navigation. The method specifically comprises the following steps: collecting remote sensing images of a semiarid region through a satellite, analyzing pixel radiation and brightness gradient to identify bare land water features, constructing a leading atlas, extracting a boundary and registering to generate a standard structure, combining a climate model to correct to generate a reflectivity layer, transferring instructions when parameters are insufficient, reconstructing a queue by using a shortest algorithm to complete radiation calibration, atmospheric correction and geometric registration, and forming a preprocessing layer set. In the application, multi-band division is combined with radiation gradient calculation to enhance the separation degree of ground objects, boundary feature extraction and geometric registration are used to eliminate imaging distortion, a dynamic radiation model is fused with climate parameters to correct reflectivity, a double-threshold value is monitored to optimize a transmission strategy and avoid interruption, a priority algorithm is used to reconstruct a queue to reduce data delay, multi-level quality control is used to cooperatively process to form standard data, and the temporal and spatial reliability and application value are improved.

The invention relates to the technical field of intelligent inspection and state diagnosis of power equipment, in particular to an intelligent recognition system for an abnormal state of a transformer. The system comprises a data acquisition unit, an environmental parameter estimation unit, an optical decoupling unit, a state diagnosis unit, an interference quantification unit and a coupling diagnosis unit. The method comprises the following steps: capturing a high-dynamic pixel flow and estimating environment parameters by a system; the core of the method is that reverse optical transmission decoupling is adopted, and a pixel stream is physically decomposed into an equipment inherent reflectivity graph and an environment illumination and interference graph; the system carries out defect diagnosis on the reflectivity graph, meanwhile, the interference graph is quantified to evaluate the credibility, and finally an interpretable diagnosis report is generated through coupling; according to the method, the problem that a traditional model cannot distinguish the equipment state from environment interference is solved, and the diagnosis robustness is improved.

A non-invasive device for detecting Alzheimer's Disease-associated pathologies, comprising a fundus camera comprising a spectral reflectance imaging unit that includes a broadband light source and a light sensor and a lens assembly to focus light from the broadband light source onto a fundus of an eye, and one or more processors to detect reflected and / or backscattered light from the eye, illuminated by the broadband light source, using the light sensor for determining spectral reflectance information, generate a plurality of spectral reflectance maps comprising counts of reflected and / or backscattered light at a different wavelength or a different range of wavelengths, assign a weight to each of the spectral reflectance maps, and determine one or more regions of interest from the weighted spectral reflectance maps as being a potential Alzheimer's Disease-associated pathology based on a detection of one or more biomarkers indicative of Tauopathy.

The application discloses a kind of illumination adaptive single-stage 2D multi-person pose estimation methods.The method comprises the following steps: constructing decomposition network, obtaining reflectivity image and illumination image;Channel attention mechanism is constructed, and illumination adaptive weight factor is generated;Denoising recovery network is constructed, and the optimized reflectivity image is obtained;The backbone network is constructed, and the joint backbone feature of the optimized reflectivity image and input image is extracted;Feature enhancement and fusion module are constructed, illumination adaptive feature enhancement is carried out based on joint backbone feature and illumination adaptive weight factor, and enhanced global feature is output;Key point detection head is constructed, and human key point is positioned based on enhanced global feature;Single-stage 2D multi-person pose estimation model is constructed and trained, and single-stage 2D multi-person pose estimation is carried out on image.The application can significantly improve the multi-person pose estimation detection precision in low-light scene, and does not significantly increase the calculation overhead, improves inference speed.

A kind of coral reef bottom reflectivity multispectral satellite remote sensing retrieval method belongs to satellite ocean remote sensing application technical field.Based on shallow sea single scattering radiation transfer model, through the collection of adjacent pixel pairs of different depth and different bottom type and the pixel of same bottom type (such as sandy seabed) of different depth, the optimal band rotation coefficient and the ratio of blue-green band diffuse attenuation coefficient are obtained respectively, combined with the optical deep water remote sensing reflectance data of the adjacent shallow sea area, the depth-independent reflectance equation and the bottom type-independent reflectance equation are constructed respectively, and based on the two equations, the coral reef bottom reflectivity image is obtained by joint calculation.It is suitable for high-resolution multispectral satellite remote sensing image, and auxiliary data such as water depth and water property are not required, and the blue-green band reflectivity data of coral reef bottom can be directly calculated and obtained.

A method for identifying a hand is provided. The method includes obtaining, via a ToF sensor of the electronic device, at least one image, wherein the at least one image includes a hand, and the TOF sensor emits infrared (IR) light and receives IR reflection of the IR light emitted from the ToF sensor, identifying a plurality of key points associated with the hand in the at least one image, generating a reflectivity map of the hand based on the reflection of the IR light received by the ToF sensor, identifying a plurality of regions of the hand in the at least one image using the reflectivity map and the plurality of key points, and based on the identified plurality of regions, identifying the hand as either a left hand or a right hand.

The application discloses a target micro-change monitoring method based on a multi-task neural network, and the target scene is photographed under multiple illumination conditions at two observation time points to obtain a current image set and a reference image set respectively; the surface structure diagram and the reflectivity diagram of the scene at the current time point and the surface structure diagram and the reflectivity diagram of the scene at the reference time point are calculated by using the current image set and the reference image set respectively; the obtained two groups of surface structure diagrams and reflectivity diagrams are aligned respectively; the aligned two groups of surface structure diagrams and reflectivity diagrams are input into a multi-scale change monitoring model, and finally the micro-change detection result of the target is obtained. Compared with the prior art, the application detects the change of the scene according to the three-dimensional information of the target and the reflectivity diagram, is not affected by the difference of the illumination conditions during imaging, can more accurately find the subtle change of the target, and is especially suitable for the tasks with high monitoring precision requirements such as cultural relic monitoring and precision instrument monitoring.

The application provides an underwater single-photon lidar target reconstruction method based on modal fusion and collaborative variational imaging optimization, comprising the following steps: reconstructing the original echo data of the underwater single-photon lidar into a superimposed photon number sequence corresponding to X*Y pixel points, wherein the superimposed photon number sequence corresponding to each pixel point is generated by performing photon number superposition based on a time bin grid on the multiple laser pulse echo signals received at its position, and the number M of laser pulses for performing photon number superposition on each pixel point is the same; performing depth-reflectivity estimation based on modal fusion on the superimposed photon number sequence of each pixel point to obtain a depth map and a reflectivity map of the underwater target; and optimizing the depth map and the reflectivity map of the underwater target based on a collaborative variational imaging optimization strategy. Using the method provided by the application, a high-quality reconstructed image of the underwater target can be quickly obtained.

The present invention discloses a low-light image enhancement method based on a multi-attention mechanism and Retinex, relating to the field of image processing technology, and solving the problems in the prior art of low-realism, severe noise and distortion, and local underexposure and overexposure of enhanced images. The method comprises: inputting a low-light image into a trained decomposition module to determine an illumination map component and a reflectance map component; inputting the illumination map component into a trained enhancement module to determine an enhanced illumination map component; and fusing the reflectance map component with the enhanced illumination map component to determine a processed low-light image, thereby achieving accurate noise removal, amplifying the global brightness of features, improving the enhancement effect, and making the output fused image smoother.