252 results about "Bilateral filter" patented technology

The invention relates to the field of image processing, in particular to a welding quality comprehensive evaluation method and system, and the method comprises the steps: obtaining an ultrasonic image of a welding joint, carrying out the contour detection, and extracting a defect region of the welding joint; gradient features and gray features of pixel points in the defect area are calculated, feature vectors of target pixel points in the defect area are constructed, and the feature vectors serve as the expression degree of the defect area; edge information of the defect area is obtained so as to determine defect types, the defect types comprise cracks and bubbles, the shape and size of a window are set according to the defect types, bilateral filtering processing is carried out, defect information is extracted, and comprehensive evaluation is carried out on the welding quality. According to the method, the appropriate window shape and size are selected according to the defect type, the detail information of the defect is effectively reserved, the definition of the ultrasonic image is remarkably enhanced through the self-adaptive filtering strategy, and therefore the welding quality is better evaluated.

The invention provides an image quality enhancement method and system based on a cloud game, and the method comprises the following steps: firstly, a cloud end renders a game picture and generates an initial video stream; then, performing preliminary optimization on the video stream, and reducing jumping and frame loss through motion compensation of an inter-frame motion vector; the optimized video stream is transmitted to a user side, after the user side receives the video stream, image quality enhancement processing is carried out, specifically, denoising processing is carried out, and random noise and compression noise are eliminated by combining an inter-frame difference method and spatial domain bilateral filtering; sharpening enhancement: enhancing image edges and texture details by using a Laplacian operator; artifact removal: removing compression artifacts through frequency domain filtering; the dynamic contrast is enhanced, and gray level distribution is adjusted through histogram equalization; finally, the video stream after enhancement processing is presented to a user in real time, the image quality of the cloud game can be remarkably improved by gradually updating the processing result of each pixel point, the video stream is presented in real time in a high-quality mode, and the user experience is optimized.

The invention discloses a medicine image detection method based on an improved Canny operator, and the method comprises the steps: 1) collecting a medicine image, and carrying out the graying processing of the medicine image; 2) an improved bilateral filter is adopted to filter the medicine image after graying processing, and noise is suppressed; 3) carrying out gradient amplitude and phase extraction on the medicine image by adopting a four-direction Soble operator; 4) performing non-maximum suppression on the extracted gradient amplitude and phase by adopting improved non-maximum suppression, and accurately positioning a target edge; 5) performing adaptive threshold extraction by using Otsu improved based on gradient edge information to obtain a drug image edge contour; and 6) outputting the edge contour of the medicine image. By adjusting the weight value of the edge pixel point identified by the bilateral filter to be zero, the interference to the central pixel point is effectively reduced, so that the target edge and detail features of the image are reserved to the greatest extent.

The invention discloses a space target point cloud three-dimensional reconstruction and rendering method, belongs to the field of space target scene reconstruction and rendering, solves the problem of low accuracy of space target scene reconstruction and rendering, and comprises the following steps: generating an initial sparse point cloud and a camera pose of a space target by using an SFM; performing denoising and outlier processing on the initial sparse point cloud by using a bilateral filtering algorithm to obtain an initial point cloud; distributing an initial gray Gaussian volume for each point in the initial point cloud, and projecting the initial gray Gaussian volume to a two-dimensional plane by using a projection formula in combination with a camera pose; rendering the two-dimensional projection image by using a micro-rasterization rendering technology; fusing the luminosity loss function, the structural similarity loss function and the depth loss function to construct a total loss function, and optimizing an initial gray Gaussian body attribute in the rendered image by using a stochastic gradient descent method; a self-adaptive density control mechanism is introduced to adjust the density of the initial point cloud, and a space target point cloud three-dimensional reconstruction structure is generated; according to the invention, the reconstruction and rendering accuracy of the space target scene is improved.

The invention belongs to the technical field of image fusion, and particularly relates to an infrared and low-light-level image fusion improvement method based on rolling guide filtering, which comprises the following steps of S1, acquiring a low-light-level image and an infrared image, and preprocessing the infrared image for denoising; s2, converting to an IHS space to extract an intensity component; s3, decomposing the dual-source I component into a small-scale layer, a large-scale layer and a base layer by using rolling guide filtering; s4, performing small-scale layer fusion, and combining an absolute value maximization strategy with non-local similarity weighted denoising; s5, performing large-scale layer fusion, and performing joint sparse low-rank representation by adopting principal component analysis; s6, fusion of the base layer adopts a visual saliency mapping method; and S7, after IHS inverse transformation reconstruction, realizing multi-level fusion in combination with bilateral filtering detail enhancement and a guide filtering saliency map. According to the invention, based on IHS space and rolling guide filtering multi-scale decomposition, the effect of cooperative utilization of multi-modal information is improved, and the method effectively improves the problems of insufficient target detection precision and poor scene reconstruction robustness in a dark environment.

The invention discloses an image super-resolution reconstruction method based on compressed sensing, and belongs to the field of image reconstruction. The problems that the updating speed of existing hardware cannot meet the requirement for signal precision, and on the premise that the hardware cost is not increased, an image with higher definition cannot be reconstructed are solved. The invention discloses an image super-resolution reconstruction method based on compressed sensing, and the method comprises the steps: designing a dictionary learning process, and obtaining high-frequency component parameters needed in image restoration; comprising the steps of constructing a training data set and performing dictionary training; wherein a corresponding low-resolution image is obtained by carrying out quality reduction and scale reduction calculation on an image so as to carry out a process of training a data set; constructing an over-complete dictionary pair by using a K-SVD algorithm, and training the dictionary; firstly, a reduced low-frequency component is obtained, and then the resolution is reconstructed. According to the method, the GPSR algorithm is utilized, the limitation of fixed sparseness of a traditional OMP algorithm is overcome, a more accurate sparse coefficient is obtained, and the precision of the reconstructed image is further improved by adopting a bilateral filtering mode.

The invention provides a dark image preprocessing method and device based on machine vision, equipment and a medium, and relates to the technical field of agricultural automation, and the method comprises the steps: constructing a multi-scale Retinex enhanced fusion adaptive gamma correction illumination compensation model, carrying out the processing of an original garlic seed screening image through the illumination compensation model, eliminating illumination unevenness and light reflection interference, and obtaining an original garlic seed screening image; obtaining a first garlic seed screening image; according to a texture complexity detection method based on a gray-scale guide map, designing an adaptive joint bilateral filter, and processing the first garlic seed screening image through the adaptive joint bilateral filter to obtain a second garlic seed screening image; and performing multi-resolution contrast enhancement processing on the second garlic seed screening image through a pyramid decomposition and entropy-driven partitioning strategy to obtain a preprocessed garlic seed screening image. According to the method, the defect detection accuracy and the classification efficiency can be effectively improved under the conditions of high noise and complex backgrounds, and robust visual preprocessing support is provided for subsequent automatic sorting.

The invention provides a marine radar image denoising method and device based on a multistage filtering algorithm, and the method comprises the steps: carrying out the preprocessing of a to-be-processed marine radar image, and obtaining a first image; performing edge gradient detection on the first image based on the gradient direction corresponding to each pixel point in the first image and the target identified in the first image, and performing noise detection on the first image based on the gray value corresponding to each pixel point in the first image after the edge gradient detection is passed; after the noise detection is passed, processing the first image by adopting bilateral filtering to obtain a second image; and image enhancement is carried out after image sharpening is carried out on the second image, and a denoised marine radar image is obtained. According to the method, multiple filtering technologies such as Gaussian filtering, median filtering and bilateral filtering are combined, multiple kinds of noise in the marine radar image are effectively removed, especially image edge and target information can be kept in a complex environment, and the target detection and navigation precision is remarkably improved.

The invention discloses a submerged chain conveyor liquid level measuring method and device, a storage medium and electronic equipment, and relates to the technical field of data processing.The method comprises the steps that image data, collected by an industrial camera, of a liquid level ruler in a submerged chain conveyor slag pool is obtained; preprocessing the image data based on a bilateral filter to obtain recognizable image data; the recognizable image data are input into a preset deep learning model for image recognition, point location information of the liquid level ruler is obtained, and the preset deep learning model is used for recognizing the point location information, contained in the image, of the liquid level ruler; and the liquid level reading of the liquid level ruler is determined according to the point location information. Compared with the prior art, the real-time image can be processed by utilizing a computer image recognition technology, the working safety is greatly improved while the measurement accuracy is improved, the environmental pollution and corrosion conditions of measurement components are greatly reduced, the labor and equipment cost can be reduced, and the measurement accuracy is improved. And the accuracy of the measured liquid level can be improved.

The invention discloses a sports runway pavement damage identification method. The method comprises the following steps: establishing a construction defect template and a use wear template; controlling the unmanned aerial vehicle to fly along the geometrical shape of the runway through a preset flight path, adopting automatic exposure adjustment and shutter speed optimization to adapt to cloudy or strong light conditions, and collecting high-resolution runway surface images with the pixel resolution reaching the micron level to obtain a first image set; for the first image set, smoothing light interference noise by adopting bilateral filtering processing and reserving edge details, and correcting the influence of strong light reflection or shadow through pixel value normalization processing to obtain a second image set; and extracting defect features from the second image set, performing multi-scale feature analysis by adopting a convolutional neural network, calculating a crack length, a recess area and a bump height, generating a high-dimensional feature vector containing defect shapes and textures, and obtaining a defect feature set.

The invention relates to the technical field of image processing, in particular to an injection molding panel production quality detection method and system based on image enhancement. The method comprises the following steps: calculating the throwing degree of each pixel point; taking the product of the throwing degree and the standard deviation preset in the bilateral filter as the optimal standard deviation of each pixel point; processing and detecting the image based on the optimal standard deviation; the step of calculating the throwing degree of each pixel point comprises the following steps of: calculating the neighborhood gray scale confusion degree of each pixel point; obtaining a point cluster containing edge information in the image, and calculating gradient consistency between pixel points in the point cluster and other pixel points in the point cluster; setting a weight coefficient of gradient consistency, and determining the regularity of the pixel point by taking the weighted sum of a plurality of gradient consistency as a total trend; and taking the ratio of the gradient law degree corresponding to the pixel point to the neighborhood gray scale confusion degree as the throwing degree of the pixel point. The method and the device have the effect of improving the accuracy of subsequent product anomaly detection.

The invention relates to the technical field of insulator measurement, in particular to an insulator appearance parameter detection method and system based on RGB point cloud data. According to the method, denoising is performed on an original RGB point cloud image set of the side surface of a target insulator through a bilateral filtering algorithm, feature extraction is performed through an FPFH algorithm, and point cloud registration processing is performed through an ICP algorithm, so that a high-quality side surface global point cloud image is obtained; then, carrying out classification processing on the side global point cloud image by adopting a PointNet algorithm, and only retaining point clouds belonging to the insulated blade part; performing curved surface reconstruction on the point cloud of the insulating blade part through a B-spline curve curved surface fitting algorithm, and further combining into a three-dimensional model of the insulating blade part; and finally, projecting the three-dimensional model into a two-dimensional point cloud, calculating an edge point cloud connection line based on the two-dimensional point cloud, and further calculating a creepage distance and an overall structure distance. Based on various algorithms, the appearance parameters of the insulator can be automatically calculated.

The invention discloses an unmanned aerial vehicle small target detection method based on Mama feature fusion, and relates to the technical field of computer vision, and the method comprises the steps: multi-modal data collection, synchronous collection of images and point cloud data through three types of sensors, and coverage of multi-scene and environment conditions; image preprocessing adopts improved bilateral filtering, adaptive histogram equalization and point cloud downsampling to unify a coordinate system; in the multi-scale feature extraction, five-level scale features are output through an improved CSPDarknet network, and the five-level scale features are enhanced through an SENet attention module; in the Mama feature fusion, multi-modal and multi-scale features are processed through a three-stage unit; generating a candidate frame by a dynamic anchor frame, screening according to IOU, and improving YOLOHead to realize classification and positioning; the dynamic optimization of the detection result finely adjusts parameters through on-line distillation. According to the method, the precision, the real-time performance and the anti-interference capability of small target detection of the unmanned aerial vehicle are improved, and reliable technical support is provided for low-altitude security, exploration and other scenes.

A video coder may receive video data, and apply a filter to a plurality of categories of samples of the video data. The plurality of categories of samples may include two or more of an input of a fixed filter, an output of the fixed filter, an input of a signaled filter, an output of the signaled filter, an input of an adaptive loop filter, an output of the adaptive loop filter, an input of a sample adaptive offset (SAO) filter, an output of the SAO filter, an input of a bilateral filter, an output of the bilateral filter, an input of a cross component SAO filter, an output of the cross component SAO filter, an input of a deblocking filter, an output of the deblocking filter, filtered reconstructed residual data, dequantized coefficients, filtered dequantized coefficients, a predictor, or a filtered predictor.

The invention discloses a method for detecting surface defects of a metal-coated crystal oscillator, which comprises the following steps of: acquiring an initial image of the metal-coated crystal oscillator in real time, and carrying out primary cutting according to coordinates to obtain an image B; converting the image B into a grey-scale map and then carrying out Gaussian filtering to obtain an original image C; setting a fixed threshold, and performing threshold segmentation on the original image C to obtain an image D; performing Hough circle detection on the image D to obtain a circle center coordinate and a radius of the crystal oscillator, and cutting the image B again to obtain an image M; the image M is amplified in proportion and then subjected to bilateral filtering processing, an image E is obtained, electrode deviation detection and surface dirty spot detection are conducted on the image E, and electrode deviation comprises electrode shape deviation and electrode eccentric defects. According to the method, the quantity demanded for samples is small, the problem that dependency on a data set is high is solved, meanwhile, recognizable defect types are added, different detection requirements can be met by adjusting parameters, and result visualization is achieved.

The invention provides a chromosome image enhancement method and system based on semantic guidance. The method comprises the following steps: S1, preprocessing; s2, outputting a deep-band probability graph, a grey-band probability graph and a shallow-band probability graph through a semantic segmentation network composed of a lightweight encoder and a multi-scale decoder; s3, implementing differential layered enhancement according to a band type: adopting local adaptive histogram equalization for a deep band, adopting central axis constraint bilateral filtering for a gray band, adopting dynamic threshold truncation and gamma correction for a shallow band, and performing weighted fusion for a transition region according to probability; s4, carrying out structure strengthening, wherein the structure strengthening comprises centromere local sharpening, stripe phase alignment, edge sensing super-resolution and overlapping region separation; and S5, performing quality evaluation based on the deep band integrity, the band stripe contrast uniformity, the SSIM and the noise density, and triggering adaptive re-enhancement if necessary. According to the scheme, the contrast ratio and details are remarkably improved while the stripe structure and the position relation are kept, and the method has the advantages of light weight, interpretability and cross-sample robustness and is suitable for being integrated into an automatic karyotype analysis process.

The invention relates to an adaptive weighted bilateral filtering deconvolution OCT image processing method and system, and the method comprises the steps: carrying out the preprocessing of an original OCT signal, including background noise removal, wave number calibration, spectrum shaping and dispersion compensation, so as to obtain an OCT reconstructed image; enhancing the contrast ratio through histogram stretching, adjusting the space weight and the intensity weight of the OCT stretching image, and performing adaptive denoising on the image; and further optimizing the image by using a blind deconvolution algorithm, and iteratively updating the estimated image and a point spread function (PSF) to obtain a high-definition OCT reconstructed image. According to the method, efficient denoising and detail reservation of the OCT image are realized through adaptive weighted bilateral filtering and blind deconvolution technologies, so that a high-definition reconstructed image is obtained.

The invention discloses an electronic screen film surface dust detection method based on photometric stereo. The method comprises the following steps: image acquisition: acquiring 4-8 grayscale images in different incident directions; image preprocessing: carrying out edge-preserving noise reduction processing on the original image by adopting a bilateral filter, improving the smoothness by combining a Gaussian filter, and converting the filtered image into a floating point type; normal reconstruction: calculating a surface normal vector corresponding to each pixel point through a least square method or robust estimation according to a photometric stereo principle by utilizing a multi-light source matrix form, and generating a normal distribution diagram; curvature calculation: estimating a local curved surface of each pixel neighborhood based on the reconstructed normal graph; and dust identification: through setting a dynamic threshold and a region growing algorithm, extracting a continuous and significant normal disturbance region, and carrying out classification, marking and visual output in combination with characteristics such as area, length, boundary curvature and the like.

The invention discloses a long-distance binocular camera calibration optimization method based on multistage feature enhancement, and the method comprises the steps: carrying out the processing of a condition that a long-distance calibration plate has a highlight region and is fuzzy in angular points, employing a bilateral filter to suppress the reflection of a highlight mirror surface, reducing the reflection of light, and maintaining the definition of an image; the checkerboard texture is enhanced by adopting a CLAHE algorithm in combination with blocking processing and a contrast gain threshold value; a Sobel operator and a Harris corner response function are fused, gradient direction distribution characteristics of a pixel neighborhood are extracted through the Sobel operator, weighted fusion is carried out on the gradient direction distribution characteristics and the Harris corner response function, and the response intensity and specificity of a corner area are remarkably enhanced. A three-level image preprocessing framework including reflection suppression, contrast enhancement and corner enhancement is constructed, the problem of feature extraction of a traditional method under a complex illumination condition is solved, the corner area detection capability is enhanced, the problems of specular reflection noise, low contrast and corner blur are effectively solved, and the calibration precision is improved.

The invention relates to a bilateral filtering remote sensing image denoising method based on an EMD distance, and the method comprises the steps: firstly, carrying out the clustering analysis of an image polluted by noise, extracting a key spectrum cluster and a center thereof, and generating a fuzzy membership matrix; thirdly, calculating the distance between spectral clustering centers, and constructing a spectral clustering difference matrix; for each pixel of the target image and a neighborhood thereof, calculating an Euclidean distance between the pixels, and substituting the Euclidean distance into a Gaussian function to generate a distance weight; then, extracting fuzzy membership degree vectors corresponding to the pixels from the fuzzy membership degree matrix, and calculating an EMD distance by combining the spectral clustering difference matrix as a spectral difference weight between the pixels; and finally, synthesizing the spectral difference weight and the distance weight, calculating an EMD bilateral filtering coefficient of each pixel, and carrying out EMD bilateral filtering on the image according to the EMD bilateral filtering coefficient, so as to effectively remove Gaussian noise and obtain a clear high-resolution satellite image.

The invention relates to the field of image processing, in particular to an infrared image bilateral filtering method based on field programmable gate array (FPGA) optimization, which comprises the following steps: acquiring data through a hierarchical architecture of'on-chip BRAM cache + distributed register cache ', intercepting pixels in combination with a sliding window, calculating a spatial weight by using an LUT look-up table, and logically calculating a gray scale weight in real time. And carrying out weighted summation and normalization output. According to the method, the processing delay of the infrared image with the resolution of 1024 * 1024 is reduced to be less than 50ms, the frame rate is increased to be more than 60fps, and the method is suitable for a real-time target recognition scene. Through the combination of the optimized bilateral filtering algorithm and the efficient hardware architecture, the processing delay is remarkably reduced, the real-time processing of the high-resolution infrared image is realized, and the real-time performance is remarkably improved.

The invention discloses a road asset automatic identification and modeling system based on laser point cloud, which belongs to the technical field of road modeling and comprises an infrastructure layer, a data storage layer, an application support layer, a business application layer, an interaction layer and a user layer. According to the invention, the point cloud processing efficiency and the road asset classification and identification precision are improved by improving and fusing the point cloud processing technology, the classification and identification technology and the automatic hooking technology; adopting a grid thinning algorithm to realize point cloud data thinning; a bilateral filtering algorithm and a Gaussian filtering algorithm are adopted to realize a point cloud denoising function, so that the point cloud processing efficiency is improved; various algorithms are fused, point cloud processing and automatic modeling technologies are improved, and the accuracy of automatic classification modeling is improved to 90% or above; automatic modeling is introduced, point cloud processing, classification identification and automatic modeling technologies are fused, the process is automatically linked, and the construction period is shortened by more than half.

The invention provides an aero-engine main bearing micro-defect nonlinear phased array ultrasonic nondestructive testing method and device, and relates to the technical field of ultrasonic nondestructive testing, the method comprises the following steps: according to the sound beam focal spot diameter of the emission aperture of a phased array excitation probe, carrying out grid division on a target detection area; according to a focusing rule of a phased array emission aperture, focusing scanning of each grid is completed in sequence, and ultrasonic signals of each grid are collected through a phased array receiving probe in the scanning process; carrying out noise reduction processing on the ultrasonic signal by utilizing a self-adaptive bilateral filtering algorithm; a signal blind source separation algorithm is adopted to decouple the processing signals, nonlinear harmonic signals generated by the microdefects are separated, and positioning and quantitative analysis are conducted on the microdefects; and the two phased array probes scan along the contour of the cross section of the bearing ring to complete the full-coverage detection of each cross section of the bearing ring. According to the invention, the microdefect of the aircraft engine bearing can be subjected to nondestructive detection.

The invention relates to an underwater image enhancement method and device based on dark channel prior, and belongs to the technical field of image processing. A dark channel prior algorithm is called, and an original underwater image is restored to a preliminary defogged image; performing bilateral filtering processing on the transmissivity image calculated in the preliminary defogged image recovery process to obtain a defogged image; weighting different color channels in the defogged image according to the absorption intensity of a water body to different colors, fusing the channel information of all the weighted color channels, reconstructing a final image, and determining the final image as an enhanced original underwater image. And moreover, blurring caused by scattering is effectively removed, the real color of the image is recovered, the image definition and contrast are improved, and meanwhile, the operation simplicity and high processing efficiency are kept.

Systems, methods, and instrumentalities are disclosed herein for determining whether to apply a filter and / or the parameters of a filter to be applied to a video block based on depth and / or motion information associated with the video block. The determination may be made based on the existence or non-existence of a depth discontinuity in the video block. The filter may be a bilateral filter, a deblocking filter, a sample adaptive offset (SAO) filter, a cross-component sample adaptive offset (CC-SAO) filter, or an adaptive loop filter.

The invention provides a tubular belt conveyor tube expansion detecting and adjusting method and device. A camera fixed at the upper end of a pipe belt machine is used for obtaining a real-time video, after preprocessing of perspective transformation, gray level conversion, bilateral filtering noise reduction and the like is conducted, edge detection is conducted by combining a self-adaptive multi-scale wavelet edge detection algorithm with a one-way edge detection operator, and an edge straight line is fitted through an LSD algorithm to calculate the pipe diameter. When pipe expansion is detected, the pipe diameter adjusting device is driven to adjust the pipe diameter, and closed-loop operation is formed. According to the method, the edge detection precision is improved by improving the wavelet edge detection method, the edge position of the pipe belt can be detected in real time, contact abrasion is avoided, the pipe expansion problem is solved in real time, and the labor cost and economic loss are reduced.

The invention relates to the technical field of intelligent control in the converter steelmaking process, and particularly discloses a converter steelmaking furnace mouth pressure intelligent balancing method with self-adaptive image segmentation, which comprises the following steps: image acquisition: acquiring a furnace mouth flame image in real time through an industrial camera; image preprocessing: adopting a dynamic gamma correction algorithm to adaptively adjust a correction coefficient according to an R channel mean value of the image; in the flame feature extraction stage, the corrected image is subjected to bilateral filtering to remove noise, and then an improved OTSU segmentation algorithm is input to extract a flame main reaction area; in the pressure intelligent control stage, a flame characteristic-furnace pressure nonlinear model is constructed, and finally the rotating speed of an ID fan is dynamically adjusted through a fuzzy self-adaptive controller; the furnace mouth flame image is intelligently monitored, then the furnace mouth pressure is analyzed, the system pressure fluctuation standard deviation is controlled to be reduced, the gas recovery rate is improved, the false detection rate is reduced, the energy efficiency of converter steelmaking is improved, and the production safety is improved.

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

The invention discloses an infrared image column stripe heterogeneity correction method based on an improved median histogram, and belongs to the field of infrared image heterogeneity correction. The method comprises the following steps: calculating a column histogram of a single-frame infrared image and a column mean value curve subjected to wavelet denoising according to an input infrared image; performing inversion operation according to the obtained column histogram to obtain a column inverse histogram; generating an adaptive window of the bilateral filter according to the column mean value curve; generating a bilateral filter according to the spatial domain distance of the column and the gray domain difference of the column mean value curve; filtering the column inverse histogram according to the obtained bilateral filter to obtain a median histogram; and an input infrared image is mapped according to the median histogram to obtain an infrared image after stripe removal. According to the method, a better fringe removal effect is achieved, blind pixel points are avoided, and the step of repeatedly calculating a comparison result to solve an optimal parameter value in an original algorithm is omitted.