82 results about "Bilateral filter" patented technology

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.

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 the technical field of image data processing, in particular to a rubberizing quality detection method and device for automatic rubberizing equipment, and the method comprises the steps: obtaining an original image of a to-be-detected rubberizing region; performing filtering processing on the original image by using an improved adaptive bilateral filtering algorithm to obtain a filtered image, performing differential operation on the filtered image and the original image to obtain a residual image, and performing adaptive threshold segmentation on the residual image to extract a defect region, and carrying out rubberizing quality detection based on the geometrical characteristics of the defect area. The problem that the accuracy of rubberizing quality detection is not high is solved.

A mechanism for processing video data is disclosed. The mechanism determines to apply a bilateral filter and a cross component sample adaptive offset (CCSAO) filter to samples in a current block of a current picture. The bilateral filter includes filter weights that vary based on a distance between surrounding samples and a central sample and differences in intensities of the surrounding samples and the central sample. A conversion is performed between a visual media data and a bitstream based on the bilateral filter and the CCSAO filter.

The invention belongs to the technical field of three-dimensional modeling, and particularly relates to a three-dimensional visual detection method for motorcycle tires. According to the method, a tire is fixed, multi-view point cloud scanning is carried out, and an improved SAC-IA algorithm is adopted for registration and fusion to construct a three-dimensional point cloud model; the method comprises the following steps: designing adaptive voxel grid filtering and denoising based on tire geometric prior, and after improved bilateral filtering smoothing, fitting and unifying a coordinate system through principal component analysis and least square to obtain a standardized point cloud model; and extracting four core parameters of thickness uniformity and sidewall contour curvature, inputting the four core parameters into the hybrid dynamic weight scoring network to output quality scores, and meeting the requirements of high-precision and high-efficiency quality inspection of motorcycle tires in an industrial environment.

The application discloses a UAV autonomous obstacle avoidance method and system based on vision-laser radar fusion, belongs to the technical field of UAV autonomous flight, and comprises the following steps: S1, collecting vision image data and laser point cloud data of the environment around a UAV through a vision sensor and a laser radar synchronously; adopting a hardware trigger and a timestamp double synchronization mechanism to control the time synchronization error of the vision image data and the laser point cloud data within + / -1 mu s; respectively carrying out denoising, enhancement and distortion correction processing on the vision image data; and adopting a bilateral filtering algorithm to remove image noise. The hardware trigger and the timestamp double synchronization mechanism are adopted to control the time synchronization error of the vision and the laser radar data within + / -1 mu s, the problems of fusion feature space dislocation and obstacle calibration distortion caused by a traditional synchronization mode are solved, a high-precision data foundation is laid for multi-sensor feature fusion, and the real-time sensing demand of high-speed flight of the UAV is met.

The present application belongs to the technical field of image data processing, and particularly relates to a defoaming sand washing monitoring method and system based on image analysis, which comprises the following steps: constructing a structure tensor matrix of a pixel point, determining the flow direction coherence degree of the pixel point, determining the oil phase anisotropy confidence factor of the pixel point, determining the optimized anisotropy space kernel parameter of the pixel point, obtaining an enhanced sand washing outlet liquid discharge image, determining the area proportion of the oil phase region in the enhanced sand washing outlet liquid discharge image, and judging whether there is oil entrainment abnormality at the discharge port. By analyzing the gray scale characteristics of the pixel point and the pixel points in its neighborhood, and by adapting the anisotropy space kernel parameter, the present application overcomes the limitations of the traditional bilateral filtering algorithm using a fixed shape filtering kernel under a high-speed mixed flow field, realizes oil phase feature enhancement, and improves the accuracy and robustness of oil entrainment abnormality monitoring in the defoaming sand washing process.

This invention presents an enhanced micro-operation target segmentation method utilizing improved Otsu and edge operators, addressing the limitations of conventional techniques in microscopic visual environments. The proposed approach effectively mitigates accuracy constraints caused by image noise and shadow effects from uneven illumination. The methodology comprises three key phases: Applying an optimized bilateral filtering algorithm to perform noise reduction on grayscale images, producing denoised images; Segmenting micro-operation targets using the refined Otsu algorithm on these denoised images to obtain initial segmentation results; Directly applying the final segmentation result when meeting predefined criteria, or conducting iterative segmentation through the enhanced edge operator and Otsu algorithm if necessary. This innovative method demonstrates exceptional performance in micro-operation target detection applications

A method for filtering an input sample. The method includes obtaining a first input sample (SIN1) (e.g., one sample included in a TU) belonging to a first transform unit (TU) having a first dimension (e.g., width) and a second dimension (e.g., height). The method further includes calculating a first BIF offset ([delta] BIF1) for the first input sample using a first TU scaling factor (CTU1) associated with the first TU and a bilateral filter (BIF) function. The method further includes calculating a first output sample (Sout1) using the [delta] BIF1 and the SIN1. The first TU scaling factor is a function of 1) a two-dimensional (2D) lookup table (LUT), and sizes of a first dimension and a second dimension of the first TU, and / or 2) an activity score (e.g., MAD, MSD) of the first TU, and / or the BIF function is a sampled segment-by-segment linear function.

A video coder may receive video data and apply a filter to a plurality of types of samples of the video data. The plurality of types of samples may include two or more of: an input of a fixed filter, an output of a fixed filter, an input of a signaled filter, an output of a signaled filter, an input of an adaptive loop filter, an output of an adaptive loop filter, an input of a sample adaptive offset (SAO) filter, the method comprises the following steps: receiving the input of an SAO filter, the output of an SAO filter, the input of a bilateral filter, the output of the bilateral filter, the input of a cross component SAO filter, the output of the cross component SAO filter, the input of a deblocking filter, the output of the deblocking filter, filtered reconstructed residual data, a dequantization coefficient, a filtered dequantization coefficient, a predictor or a filtered predictor.

The invention discloses a loop filtering method and device for reconstructing a video. The method receives input data of a current block, wherein the input data includes reconstructed samples of the current block. At least two loop filters are applied to a current block, where the at least two loop filters belong to a loop filter bank comprising a bilateral filter (BIF) and an adaptive loop filter (ALF), and the classification processes of the at least two loop filters share one input source, one or more classification rules, one or more processing units or a combination thereof. A filtered output generated by applying the at least two loop filters to the current block is provided.

A kind of blood vessel image enhancement method based on multiscale Gaussian filter, comprising the following steps: step one: input endoscopic blood vessel image;Step two: the input blood vessel image is separated into RGB three channel planes;Step three: using multiscale Gaussian filter and bilateral filter to filter G component to obtain detail layer and base layer;Step four: detail layer is multiplied by a gain coefficient, then merged with base layer to obtain two enhancement results;Step five: two enhancement results are converted to HIS space, I component is carried out 4-layer wavelet decomposition to obtain 1 low frequency and 12 high frequency components, and the rule of high frequency weighted average low frequency modulus value maximum is used to fuse to obtain enhanced I component, then merged with HS, converted to RGB space to obtain the final enhancement result.The present application can carry out different adaptive enhancement to different levels of blood vessels to obtain more abundant blood vessel images, which can assist doctors in early diagnosis of cancer.

The application discloses a kind of self-adapting shadow enhancement method and device of smoke-passed infrared digital holographic reconstructed image, belong to image processing technical field.The application proposes a new image processing algorithm to enhance the shadow area caused by smoke-passed infrared digital holographic reconstructed image smoke sheltering, the algorithm is first using bilateral filter to remove noise to image, in combination with binaryzation, edge extraction, morphological algorithm divides shadow area, and in combination with bright area to achieve the purpose of enhancing the brightness of shadow area.The method disclosed in the application solves the problems of severe speckle noise, obvious shadow area, uneven brightness, missing object detail information and other problems of smoke-passed infrared holographic reconstructed image, can effectively enhance the image quality of smoke-passed infrared holographic reconstructed image, has significant effect in the aspects of shadow area enhancement and speckle noise removal, and belongs to the field of image processing technology.

The invention provides a railway track crack defect detection method and device based on machine vision, and relates to the technical field of crack detection, and the method comprises the steps: obtaining an original track image; preprocessing the original track image, and performing image enhancement in combination with a bilateral filtering strategy to obtain a track image with a high signal-to-noise ratio; based on a convolution attention mechanism, improving the target detection model in combination with multi-scale feature fusion and a residual connection strategy to obtain an improved target detection model; inputting the track image with the high signal-to-noise ratio into the improved target detection model for track crack detection to obtain a crack segmentation mask graph; performing morphological processing on the crack segmentation mask graph, and obtaining a crack image frame by extracting geometrical characteristics of the crack; and associating adjacent crack image frames according to a continuous frame matching algorithm, and constructing a space-time development track of the track crack by combining the space position information of the crack as a defect detection result. The problem that the rail operation safety cannot be effectively guaranteed is solved.

The application discloses a virtual object matching method and device in a complex environment, comprising: determining a sparse three-dimensional point cloud of a target based on a camera pose, and obtaining depth image (RGB-D) information of the target; combining the RGB-D information with the sparse three-dimensional point cloud to obtain a dense three-dimensional point cloud; removing outliers in the dense three-dimensional point cloud by using a specified bilateral filter, and weighting adjacent points by using an improved Gaussian weighting function; sampling based on the dense three-dimensional point cloud and a normal vector of each point calculated to obtain key points; calculating feature descriptors of a virtual model and a real scene based on the key points obtained by point cloud sampling to estimate the corresponding relationship between the two point clouds; and completing transformation estimation according to the corresponding relationship to realize matching of the virtual object and the real scene. The method can improve the stability, accuracy and real-time performance of matching of the virtual object and the real object in markerless augmented reality.

The application discloses a camera calibration method and system under uneven illumination conditions, and belongs to the technical field of image processing, and comprises the following steps: S1, processing an input image by using a multi-scale Retinex algorithm; S2, performing Gaussian homomorphic filtering processing on the image obtained in the step S1; S3, processing the image obtained in the step S2 by using an Otsu algorithm; and S4, performing camera calibration on the image processed in the steps S1-S3 by using Zhang Zhengyou plane calibration method. The application firstly enhances image details by using the multi-scale Retinex algorithm and the Gaussian homomorphic filtering, then obtains a final calibration board image by combining Otsu algorithm image segmentation and bilateral filtering processing, and further realizes high-precision calibration of the camera, and is worth popularization and use.

A method and apparatus for encoding or decoding a video are provided, in which reconstructed blocks of the video are obtained during encoding or decoding of the video. A filter strength of the bilateral filter is determined for the block based on at least one parameter used to encode the block. In response to the determination of the filter strength, a bilateral filter is applied to the block.

The present application provides a kind of open-pit mine step line high-precision extraction method, device and electronic equipment, wherein the method comprises: using Gaussian filter and bilateral filter to carry out denoising processing to the target model of open-pit mine;The target model includes digital surface model, digital elevation model;The second derivative of the target model after denoising is calculated, and the initial discrete points of step line are extracted based on the second derivative;The initial discrete points of step line are corrected, and the step line discrete points obtained by correction are split to obtain the step line discrete point set of different height steps;Each step line discrete point in the step line discrete point set is sorted and smoothed, and the target step line is connected to obtain. By the present application, the open-pit mine step line is extracted with high precision and automation, and the problem of weak anti-interference ability and low extraction precision in the existing related technology is solved.

The invention discloses a dispensing robot motion control system based on template matching, and belongs to the technical field of industrial automation and robot control. According to the invention, the problems of inaccurate positioning, wrong and leaked glue and track optimization existing in the existing micro object glue dispensing are solved. The method specifically comprises the following steps: preprocessing a collected image, carrying out denoising and feature enhancement on the collected image by adopting an improved bilateral filtering algorithm in combination with Haar wavelet transform, and then realizing accurate positioning by fusing color matching, gray mode matching and an improved dung beetle optimization algorithm; and finally, through D-H parametric method modeling and forward and inverse kinematics analysis of the robot, the motion trail of the robot is optimized. According to the invention, the dispensing positioning precision and matching efficiency can be effectively improved, the phenomena of glue mistake and glue leakage are reduced, and the method can be applied to high-precision dispensing operation of micro objects.

The invention discloses a line structured light center line extraction method and device based on a line structured light measurement system, and the method comprises the steps: 1, carrying out the preprocessing of an original laser stripe image through a bilateral filter, and obtaining a preprocessed image; 2, determining a low threshold value and a high threshold value, and performing regional processing on the preprocessed image by using the low threshold value and the high threshold value to obtain an ROI region; 3, extracting an initial stripe center line in the ROI region; 4, denoising and smoothing the extracted initial stripe center line to obtain a continuous and complete initial laser stripe center line; and 5, extracting to obtain the position of a sub-pixel center line, and obtaining a complete and robust line structured light sub-pixel center line. According to the method, the laser stripe region is accurately extracted by adopting the self-adaptive double-threshold segmentation method, and sub-pixel center positioning is carried out in combination with the improved gray scale weight model, so that the center positioning precision and the anti-noise capability are effectively improved.

The application discloses a low-illumination image enhancement method based on an improved Retinex algorithm, and applies to the technical field of image enhancement, and comprises the following steps: performing discrete wavelet decomposition on a low-illumination image to obtain low-frequency and high-frequency components of the image; converting the low-frequency component into HSV, separately performing brightness correction on a V channel, then converting back to RGB, performing bilateral filtering, and then converting to HSV to extract the V channel; performing image enhancement on the low-frequency component by using an improved Retinex algorithm based on a joint weighting of a bilateral filter and a Gaussian filter as a new center-surround function, and performing median filtering processing, then converting to HSV to extract the V channel; weighting and fusing the two V channels, retaining the H and S channels after algorithm enhancement, then converting back to RGB, performing discrete wavelet fusion with the high-frequency component after denoising, and stretching and outputting the enhanced low-illumination image. The application can effectively guarantee the color, edge details and avoid local distortion of the image.

An apparatus configured to code video data may reconstruct a block of video data, determine a filter class for samples of the block, determine one or more filter sub-classes for samples in each filter class, and apply a filter to the samples in each filter sub-class, including applying a respective scaling factor to samples in each filter sub-class. The filter is an adaptive loop filter (ALF), a deblocking filter, a sample adaptive offset filter, a bilateral filter, a cross-component sample adaptive offset filter (CC-SAO), a cross-component adaptive loop filter (CC-ALF), or a neural network-based filter.

The invention provides a non-contact touch detection method and device for depth image fusion, equipment and a medium. The method comprises the steps of performing bilateral filtering processing on an original depth image to obtain a depth image, performing space-time alignment processing on an RGB image and the depth image to generate image pair data, and performing skin color region marking on the image pair data to obtain candidate skin color regions, carrying out threshold segmentation processing according to the depth image and the candidate skin color area to obtain a binary image, and carrying out edge depth gradient peak detection and contour curvature analysis on a hand area on the binary image to obtain space area data of a fingertip; and performing fingertip sliding tracking on a fingertip coordinate set in the spatial region data to obtain fingertip trajectory data, and performing touch decision analysis on the fingertip trajectory data according to a condition threshold set to generate a touch instruction signal. According to the invention, high-precision three-dimensional positioning and stable trajectory tracking of the fingertip are realized, and the robustness of noise and missing pixels is improved.

A video decoding method includes receiving a bitstream including encoded information of one or more pictures. The encoded information indicates intra prediction of the current block. The method further includes enabling reference sample filtering with a bilateral filter when the current block satisfies one or more conditions associated with the reference sample filtering; when the reference sample filtering with the bilateral filter is started, applying the reference sample filtering with the bilateral filter to the reference sample of the current block to obtain a filtered reference sample; and reconstructing one or more samples of the current block according to intra prediction using the filtered reference samples.

The invention provides a low-illumination remote sensing image reconstruction system, method and device based on ZYNQ and Retinex, and a medium, and the system is constructed on a ZYNQ platform. The system comprises an image acquisition module for acquiring a low-illumination remote sensing image, an HSV space conversion module for converting the image from an RGB space to an HSV space, a brightness enhancement module for processing a V-channel image by applying an MSR algorithm, and a bilateral filtering module for performing bilateral filtering on the V-channel image processed by the MSR algorithm. The gamma correction module is used for compensating the illumination component of the V-channel image after bilateral filtering processing by adopting a Gamma correction method conforming to human eye visual perception; and the synthesis module is used for synthesizing the H-channel image, the S-channel image and the V-channel image output by the gamma correction module. According to the invention, the color accuracy of low-illumination remote sensing image reconstruction is greatly improved.

This invention provides a method, apparatus, device, and medium for generating weak similarity based on style transfer. The method includes: S10, redrawing a style reference image to generate a clear style image; S20, extracting visual elements from the clear style image and mapping the visual elements to a target content image through an adaptive style injection mechanism to generate an initial result image; S30, optimizing the initial result image to generate an adjusted result image; S40, performing multi-scale optimization on the adjusted result image, the multi-scale optimization including image segmentation, semantic structure analysis, and semantically guided redrawing to obtain an optimized result image; S50, performing semantic region classification on the optimized result image and using bilateral filtering to assist optimization to generate a final style image. This invention solves the problem in the prior art where the use of fixed weights leads to loss of detail or over-stylization, making it difficult to achieve natural style transfer while preserving the target content structure.

A mechanism for processing video data is disclosed. The mechanism determines to apply a bilateral filter to samples in a current block of a current picture. The bilateral filter includes filter weights that vary based on a distance between surrounding samples and a central sample and differences in intensities of the surrounding samples and the central sample. A conversion is performed between a visual media data and a bitstream based on the bilateral filter.

The invention discloses a cold region concrete dam dangerous case intelligent identification method based on image information, and the method comprises the following steps: constructing a cold region concrete dam dangerous case image noise reduction method through the comprehensive application of wavelet transform, improved non-local mean noise reduction and a bilateral filtering algorithm; through improvement of a multi-scale Retinex algorithm and fusion of gamma transformation and a contrast-limited adaptive histogram equalization algorithm, a cold region concrete dam dangerous case image quality enhancement method is provided. Based on the pre-processed high-quality image, combining with an improved YOLOv8 network structure, introducing an attention mechanism to enhance the feature extraction capability, and constructing an intelligent identification model for the dangerous case of the concrete dam in the cold region; according to the method, the recognition precision of typical diseases such as cracks and freeze-thaw damage is remarkably improved.

The invention aims to provide an IPT weak and small target detection method based on improved bilateral filtering regularization, and the method comprises the following steps: S1, carrying out the preprocessing of a weak and small target tensor block image to be detected, and obtaining a preprocessed image; s2, constructing an IPT model; s3, constructing a regularization factor TV based on improved bilateral filtering; s4, constructing a corner intensity function W; s5, performing final constraint on the IPT model based on the regularization factor TV and the corner intensity function to obtain a constraint model; s6, based on the constraint model, constructing to obtain an augmented Lagrange function L; and S7, inputting the preprocessed image into an augmented Lagrangian function, carrying out iterative updating on each auxiliary variable and the Lagrangian multiplier until a convergence condition is reached, and outputting a background block image B and a target block image T which are obtained through decomposition to obtain a final result. According to the method, clutter interference in the background can be suppressed, so that the robustness of the background low rank is improved, the false alarm is reduced, and the detection rate is improved.

The application discloses an image processing method, device and equipment and a medium, and relates to the field of image processing. The method comprises the following steps: determining a first image mask corresponding to an original image; performing bilateral filtering processing on the original image to obtain a smooth image, determining target high-frequency information according to the original image and the smooth image, determining a skin-smoothing reference image based on the smooth image and the target high-frequency information, and performing regional mixing processing on the original image and the skin-smoothing reference image based on the first image mask to obtain a first target image; determining a second image mask based on a whitening degree parameter and the first image mask, and determining a second target image according to the second image mask; performing a channel separation operation on the second target image after converting the second target image from an initial color space to a target color space, combining the channels obtained after the adjustment to obtain a third target image, converting the third target image to the initial color space, and obtaining a target ruddy image; and determining a target skin-beautifying image based on the target ruddy image.