355 results about "Digital image processing" patented technology

The invention relates to the field of digital image processing and information security, and discloses a document tampering detection method and system based on image data processing, and the method comprises the following steps: firstly, extracting the noise residual error and microscopic penetration characteristics of a document image, and constructing a physical potential energy field and a virtual viscous resistance field; performing dynamic evolution by using a Darcy law variant model to generate a virtual flow velocity vector field to simulate the slippage behavior of the fluid in the heterogeneous medium; and then a heterogeneous graph is constructed based on the flow field divergence singular points and the streamline trajectory, deep reasoning is carried out by using graph neural network aggregation node dynamic features, and finally a tampering localization mask is generated. According to the method, the fluid mechanics field theory is innovatively introduced, hidden static texture differences are converted into remarkable dynamic flow field anomalies, the problem that microscopic tampering traces are difficult to capture in the prior art is solved, and the detection precision and generalization ability in a complex document scene are remarkably improved.

An apparatus and method for training and using a computing operation for digital image processing are provided. The apparatus and method may be used for 3-dimensional medical images. An exemplary method for digital image processing comprises: receiving an image displaying at least one detectable structure, determining the detectable structure; segmenting the image to obtain a segmentation mask that is associated with a geometric shape and comprises at least one quantifiable visual feature; generating a mesh based on the quantifiable visual feature; computing at least on quantifiable visual parameter based on the mesh; extracting quantifiable visual data from the image based on the quantifiable visual parameter; training the computing operation with the quantifiable visual data. The method for digital image processing further comprises: receiving another image; segmenting, generating a mesh, computing quantifiable visual parameters, and extracting quantifiable visual data; and classifying the extracted quantifiable visual data with the trained computing operation.

The embodiment of the invention provides an edge detection method and device based on gradient weighted fusion and an adaptive threshold, and is applied to the field of computer vision and digital image processing. The method comprises the steps of firstly preprocessing an input image, then extracting two groups of gradient magnitude diagrams and directional diagrams through an adaptive morphological operator and a traditional difference operator, and constructing a weighted fusion function according to the gradient direction consistency of each pixel point to obtain a fused gradient magnitude diagram; and adaptively determining a high threshold and a low threshold based on an information entropy maximization principle, executing an improved Canny process by combining the fused gradient magnitude diagram and the second gradient directional diagram to obtain an initial edge diagram, and outputting a final edge detection result after dynamic structure element optimization. In this way, the defects that in a traditional edge detection method, gradient information extraction is not precise, threshold selection lacks adaptability, and an edge result is fractured can be overcome, more robust and more accurate image edge detection is achieved, and the reliability of an edge detection algorithm in a complex image scene is improved.

The invention discloses an event-image dual-mode fusion video turbulence correction method, medium and system, and belongs to the field of digital image processing, and the method comprises the steps: synchronously obtaining an event voxel of a target and a to-be-corrected image sequence; inputting the event voxels into a pre-selected coding and decoding structure to obtain features, projecting the features along the optical flow direction, and performing three-dimensional reconstruction to obtain target motion features; extracting background scene representation; fusing the target motion feature and the background scene representation in a channel dimension to obtain an edge guide feature; inputting the image sequence and the image sequence into a pre-trained video restoration network to obtain a turbulence-corrected video; the training loss of the coding and decoding structure comprises an optical flow field estimated according to an image sequence without turbulence disturbance and a target motion field obtained by projecting the feature along the optical flow direction. The method can accelerate the recovery process and improve the recovery quality.

The invention relates to the field of digital image processing, in particular to a super-resolution imaging method based on focal plane splicing and adaptive fusion. According to the method, sub-pixel offset among nine CCDs is preset through hardware, and nine frames of low-resolution image sequences with accurate displacement are obtained in push-broom. A central image is taken as a reference frame, high-precision mapping is realized based on hardware offset, motion estimation errors are avoided, effective pixels are screened by calculating robustness weight, an anisotropic Gaussian kernel function with a self-adaptive local structure is constructed so as to maintain image edge and detail features, and each frame is accumulated to a high-resolution grid in a weighting mode, so that a high-resolution image is obtained. And a sample compensation mechanism based on cumulative robustness is introduced, a fusion strategy is adaptively adjusted in an information insufficient area, and finally a high-resolution image is generated through normalization. The method significantly improves the imaging quality, suppresses artifacts and noise, and is suitable for the field of satellite remote sensing.

The invention discloses a vision enhancement robust digital dark watermarking method based on deep learning, a storage medium and equipment, and belongs to the technical field of digital image processing and information security. According to the method, a visual enhancement module comprising a self-adaptive watermark region adjustment module and a frequency enhancement module is constructed, and a multi-region local loss training mechanism and a screen shooting simulation module are combined, so that the visual quality of a watermark image is improved, and meanwhile, the robustness of the watermark image for resisting physical attacks such as screen shooting is enhanced. The specific processing flow comprises the following steps: acquiring an original image and watermark information; embedding the watermark information into the image by using an encoder, wherein an embedded region is optimized by a self-adaptive region guide matrix generated based on the image edge and gray information; in the model training process, a noise layer simulating screen shooting physical distortion is introduced, and the area weight is dynamically adjusted according to residual error distribution in the later stage of training so as to focus and optimize the image quality; watermark information is extracted from an image which may suffer from an attack by using a decoder.

The invention discloses an ultrahigh-definition panoramic image adaptive HDR fusion method based on data driving, and relates to the technical field of digital image processing, and the method comprises the steps: carrying out the coarse alignment and high dynamic range fusion of a low-resolution line graph sequence, and generating a low-resolution HDR panoramic image; carrying out sharpening processing on the low-resolution HDR panoramic image to obtain a low-resolution sharpened HDR image, and meanwhile, obtaining a multi-scale residual spectrum generated by sharpening; performing up-sampling on the multi-scale residual spectrum, and performing small-sample element learning fine tuning on the weight prediction network in combination with the low-resolution sharpened HDR graph to obtain a fine-tuned weight prediction network; performing weight prediction and layer-by-layer fusion on the registered high-resolution tile sequence on a multi-scale pyramid based on a fine tuning weight prediction network model; according to the method, the sharpening processing is implemented on the low-resolution HDR panorama, and the multi-scale residual spectrum is extracted, so that the detail sensitivity and adaptability in a complex scene are improved.

The embodiment of the invention provides a three-dimensional space change detection method and device, equipment and a storage medium, and relates to the field of digital image processing. The method comprises the following steps: acquiring image sequences of the same area shot by the unmanned aerial vehicle in two periods; for each image sequence, performing point cloud reconstruction on the region based on the image sequence to obtain the group of point cloud data; point cloud registration is carried out according to the similarity between the feature descriptors of all the points in the two sets of point cloud data, a matching point pair set comprising a plurality of matching point pairs is obtained, and two points in each matching point pair belong to two pieces of point cloud data and are closest to each other; and for each matching point pair, geometric information of two points in the matching point pair in respective point cloud data is acquired, and if it is determined that the difference between the two pieces of geometric information meets a preset difference condition, it is determined that the corresponding position of the matching point pair in the region is changed. According to the embodiment of the invention, the problem of insufficient spatial change detection precision in a complex scene is solved.

The invention discloses a lightweight mural virtual restoration method based on an inactive generative model, and belongs to the technical field of digital image processing and computer vision. The objective of the invention is to solve the technical problem of semantic and texture recovery imbalance of an existing deep learning repair model in face of cultural heritage such as murals. A damaged image and a mask are stacked into a multi-channel tensor to be input into a pre-trained AFMNet model, the model adopts an encoder-bottleneck-decoder architecture, and an encoder uses a multi-domain partial convolution module without an activation function to synchronously extract and fuse local texture and frequency domain global structure information and synchronously update the mask; the bottleneck portion models the lowest resolution features and masks thereof through a lightweight multi-head attention module to capture remote dependencies, and a decoder upsamples in conjunction with the features of the jump connection transfer to reconstruct image details. The method is mainly used for carrying out efficient and high-fidelity digital virtual restoration on damaged visual cultural heritage such as ancient murals.

The invention belongs to the technical field of digital image processing, and particularly relates to a knowledge distillation-based cross-modal fusion lightweight defect detection method, which comprises the following steps of S10, cross-modal fusion distillation; through a bidirectional vision-language alignment mechanism, the frozen multi-modal knowledge of a teacher model vision-language basic model VLM is migrated to a lightweight student model, and the dual-path fusion module comprises text condition region representation injected with semantic context and region anchoring semantic embedding fused with spatial vision clues; step S20, cross-header word-region alignment is carried out; embedding the fusion visual features generated by the two-way fusion module and the enhanced text to generate cross-head prediction so as to simulate the semantic-space association capability of a teacher model; s30, knowledge distillation loss is fused; according to the method, the multi-modal basic model is fused and distilled into the lightweight single-modal detection model, the detection performance in a defect detection scene can be improved, and compared with the basic model, the reasoning speed is greatly improved, and the parameter quantity is reduced.

The invention belongs to the technical field of digital image processing, and discloses an image restoration method, system and device based on multi-modal large model driving. The method comprises the following steps: receiving a to-be-recovered degraded image, inputting the degraded image and a preset multi-task text cue word into a multi-modal large model, analyzing and processing the degraded image, and generating an operation instruction and a visual description prompt of the degraded image; encoding the operation instruction and the visual description prompt to respectively obtain a corresponding task intention vector and a content guide vector; and fusing the task intention vector and the content guide vector through an image restoration model, reconstructing the degraded image, and outputting a restored image. According to the method, the image problem can be automatically and accurately diagnosed, rich guidance information is provided from the two dimensions of operation and content, and intelligent, automatic and high-fidelity image recovery is realized.

The invention relates to the technical field of image recognition processing, in particular to an automatic organoid labeling method based on machine vision and deep learning, which comprises the following steps: acquiring a to-be-processed image in a high-throughput culture environment, and preprocessing the image; inputting the preprocessed to-be-processed image into a preset digital image processing framework; the digital image processing architecture comprises an image filtering unit, a three-dimensional boundary reconstruction unit and a texture gray analysis unit; generating an independent target area mask through a three-dimensional boundary reconstruction unit; generating a high-density core mask through a texture gray analysis unit; based on the target area mask and the high-density core mask, constructing a three-dimensional geometric topological graph of the organoid; according to the method, the three-dimensional space morphological characteristics of the neighborhood nodes are obtained through processing by means of a numerical iterative aggregation algorithm, and the image feature parameters are output through analysis of the three-dimensional space morphological characteristics.

The invention discloses a monitoring image multi-target tracking method based on deep learning, and relates to the technical field of digital image processing, and the method comprises the following steps: S1, generating a multi-scale feature pyramid; s2, extracting causal feature vectors; s3, generating a discrete codebook index; s4, constructing a dynamic graph; s5, inputting the dynamic graph into an improved ASTGCN network, iteratively fusing space-time attention weights and Hamilton dynamics evolution characteristics between nodes through cascaded Hamilton space-time blocks, and generating a final prediction state of each historical track node; s6, solving an optimal correlation matching matrix by using a Hungary algorithm; and S7, extracting all active track information. According to the method, the limitations of identity drift caused by variable appearance characteristics and inaccurate prediction caused by lack of physical priori in a traditional multi-target tracking method are overcome, and an efficient, accurate and robust solution is provided for intelligent video monitoring.

The invention discloses a video subtitle erasing method, device and equipment and a storage medium, and relates to the field of digital image processing, and the method comprises the steps: detecting subtitles of a subtitle video to be erased, merging timestamps of the same subtitles in the subtitle video to be erased, and determining a subtitle fragment set and a subtitle-free fragment set; splitting the subtitle segment into independent shot segments by using a preset lens splitting algorithm, and analyzing video frames of the independent shot segments to obtain a first frame and a tail frame; determining a target reference frame based on the first frame and the tail frame, generating an expanded independent shot segment according to the target reference frame and the independent shot segment, and segmenting a target character mask; and generating an erased independent lens segment according to the expanded independent lens segment and the target character mask through a preset erasure algorithm, performing a preset post-processing optimization operation on the erased independent lens segment to obtain a target independent lens segment, and integrating the target independent lens segment and the subtitle-free segment set to generate a target video. According to the method and the device, the video subtitles can be accurately erased.

The invention relates to the technical field of rock fracture mechanics and rock digital image processing, in particular to a super-high-temperature reservoir supercritical carbon dioxide fracturing visual testing technology and device which comprises an instrument fixing base, and an instrument fixing bearing table is fixedly connected to the top of the instrument fixing base. The top of the instrument fixing bearing table is fixedly connected with a hydraulic servo loading machine fixing plate, the top of the hydraulic servo loading machine fixing plate is fixedly connected with a hydraulic servo loading machine, the bottom of the hydraulic servo loading machine is slidably connected with a loading platform through a loading sliding rod, and the top of the loading platform is fixedly connected with a loading bin. According to the invention, through a loading structure formed by the hydraulic servo loading machine, the telescopic loading sleeve rod and the telescopic loading pressure head, and in combination with control of the loading control-data processing system on a loading path and a loading process, a rock test piece can undergo continuous and controllable damage and fracture evolution processes under different loading modes and different working conditions.

The invention discloses a low-illumination image enhancement method based on concave manifold projection and local entropy weight arbitration, and belongs to the technical field of digital image processing. According to the method, a deep enhancement network of concave manifold projection and local entropy weight arbitration is constructed, and the network comprises an encoder module and a decoder module. In an encoder part, a concave manifold projection mechanism based on physical constraints is introduced, a four-dimensional input vector is constructed by extracting a texture density map and an RGB channel, physical parameter features are predicted by using a semantic guidance type physical manifold parameterization module, and an illumination enhanced parameterization model is established to reconstruct illumination distribution. In a decoder part, a local entropy weight arbitration mechanism is introduced, multi-scale features are captured by using a group of parallel cavity convolution branches with different expansion rates, and adaptive weighted fusion is performed on the features through a space attention weight map generated by a local entropy guidance type multi-scale arbitration module, so that noise amplification is suppressed while image details are recovered. According to the invention, enhancement of the low-illumination image can be effectively realized.

The invention relates to the technical field of digital image processing and computer vision, and discloses a strip detection method based on image data processing, which comprises the following steps: acquiring an original image matrix of the surface of a carrier to be detected; dynamically adjusting diffusion flux according to a pixel neighborhood gradient modulus value, and processing an original image matrix to generate a reference matrix; applying spatial perturbation to the reference matrix and generating virtual residual distribution; performing differential operation on the original image matrix and the reference matrix to construct a feature matrix; and calculating an energy offset vector between the feature matrix and the virtual residual distribution, identifying a non-target noise cluster according to the linearity difference of the energy offset vector relative to a preset displacement amount, and reducing the pixel energy weight of the non-target noise cluster. According to the method, decoupling of physical entity features and transient noise features is realized, and weak damage features are reserved while non-uniform illumination and water mist noise are suppressed.

The invention relates to a method for determining the position and / or orientation of tools of construction machines in a construction area, comprising the steps of: attaching at least one stereo camera to at least one construction machine or to a movable carrier, such that the stereo camera can detect at least one detection region within the construction area, preferably a working region of the construction machine, providing at least one reference point, the position of which is known relative to a predefined 3D world coordinate system and which can be recognised in the images of the stereo camera, in the detection region of the at least one stereo camera, calibrating the at least one stereo camera on the basis of the detection of the at least one reference point and triangulation in order to ascertain a transformation between the 3D world coordinate system and a 2D image coordinate system of the at least one stereo camera, preparing a virtual digital 3D terrain model of the construction area by means of digital image processing, in particular stereoscopy, and image recognition on the basis of the detection of the construction area by means of the at least one stereo camera, and representing predetermined working positions and detected actual tool positions and / or actual tool inclinations of the tool of the at least one construction machine in the virtual digital 3D terrain model.

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 relates to the technical field of digital image processing, and discloses an image defogging method based on an atmospheric scattering model and color correction, and the method comprises the following steps: S1, carrying out the color correction of a foggy image with color cast; s2, calculating an atmospheric light value of the foggy image; s3, calculating the transmissivity value of the foggy image; s4, recovering the defogged image by adopting an atmospheric scattering model according to the atmospheric light value and the transmissivity value; and S5, solving a defogged image after adaptive brightness enhancement and adaptive contrast enhancement. According to the method, the color of the obtained defogged image can be recovered to be natural, the phenomena of color cast, incomplete defogging, detail information loss and overall darkness of the image are improved, the recovered defogged image better conforms to the perception of human eyes, meanwhile, the implementation process is simple, the time complexity is low, and better defogging efficiency is achieved.

The invention discloses a pixel extraction and conversion method of an LEN format image, and belongs to the technical field of digital image processing. In order to solve the technical problems that the LEN format has no public specification and general software cannot read data, binary analysis is carried out through an LEN sample, and the core binary layout of a file is found for the first time: the image width and height are stored in specified offset positions, and a small-end byte order is adopted; pixel data comprises a compressed data segment and a row information segment, and an RLE coding rule is adopted. The method comprises the following steps: acquiring an LEN format file; analyzing the file header to obtain an image size; performing segmented analysis on the image data; synthesizing the analyzed data into image data; and packaging the pixel matrix into a standard TIFF format file. The LEN core binary layout is disclosed for the first time, zero distortion and cross-platform conversion are achieved, reading and storage of screening parameters are not involved at all, existing patents are effectively avoided, and the method can be used for preview, filing and third-party system integration in the flexographic printing process.

The invention relates to the technical field of digital image processing, in particular to a knitted label scanning image completion operation method, which comprises the following steps of: acquiring a knitted label image and converting the knitted label image to a frequency domain, locking a texture fundamental frequency based on energy distribution, executing protective denoising, repairing a texture phase flow field, extracting a character stroke skeleton in combination with gradient analysis, and completing the completion of the knitted label scanning image. According to the method, the image is converted to the frequency domain, the texture fundamental frequency energy is locked, the periodic structure characteristics of the knitted fabric are separated and protected, continuous solution is carried out on the phase field, smooth reconstruction of the warp and weft texture manifold is achieved, the phase dislocation defect caused by local pixel matching is eliminated, and the method has the advantages of being high in accuracy and high in reliability. A comprehensive steering vector field and a character stroke skeleton path are generated through orthogonal gradient synthesis, strong geometric constraint is introduced in the pixel filling process, it is ensured that yarn textures follow the original weaving trend after repairing, and character stroke coherence and edge sharpness are ensured.

The invention relates to the technical field of oral cavity digital image processing, and provides an implant perimucosa red and swollen area detection method based on deep learning, and the method comprises the steps: S1, collecting three-dimensional model data of an implant site of a patient, and exporting a standardized visual angle rendering screenshot with a visual enhancement effect by using a matched software rendering function; s2, constructing a deep convolutional neural network based on a YOLOv8 architecture, training a model by adopting a transfer learning strategy, and realizing automatic extraction of implant perimucosa red and swollen focus features; s3, through a feature fusion module in the deep convolutional neural network, automatically retrieving suspected red and swollen sites on the feature maps with different resolutions, performing coordinate correction on the candidate region, and generating an accurate detection frame; and S4, automatically executing batch prediction on the test set based on the trained model, and outputting a detection frame and a quantitative index. And automatic identification and spatial positioning of the red and swollen mucosa area around the implant are realized, so that a visual basis is provided for clinical precise probing and diagnosis and remote early warning.

The application discloses a kind of crack segmentation image post-processing optimization and quantization method, device and equipment, it is related to computer vision and digital image processing technical field, this method includes obtaining the initial binary crack mask chart corresponding to the image to be processed, and carries out geometric morphological filtering operation to eliminate non-crack area, obtains preliminary filtered mask chart;Contour detection is carried out to the preliminary filtered mask chart, and the crack-containing region of interest mask chart is identified and cropped;Break point connection processing is carried out to the region of interest mask chart to repair discontinuous crack fragment, generate continuous optimized crack mask chart, to carry out the geometric size parameter calculation of crack.This application can significantly improve the accuracy, integrity of crack segmentation result, and can realize the automation, accurate quantization of key parameters.

The invention belongs to the field of panoramic stitching digital image processing technologies and camera ISP technologies, and particularly relates to a chromatic aberration consistency control method based on front and rear end collaboration. The method comprises the following steps: 1, acquiring statistical information of multiple cameras; step 2, acquiring a current camera control parameter by adopting a front-end chromatic aberration consistency adjustment algorithm; 3, the current camera control parameters are issued to all the cameras, and all the cameras complete exposure imaging according to the current camera control parameters; step 4, acquiring an overlapped part image in each path of camera imaging; step 5, calculating a statistical histogram cumulative distribution function of overlapped part images in each path of camera imaging; step 6, calculating a histogram matching function according to the cumulative distribution function of the overlapped part images in each path of overlapped camera imaging, so that the histograms of the overlapped parts of the two images tend to be consistent; and step 7, according to the obtained histogram matching function, transforming the brightness channel of each pixel in the corresponding camera imaging to complete color difference consistency correction.

A system to capture a plurality of two dimensional digital source images of a scene / event by a user, including a memory device for storing an instruction, a processor in communication with the memory and configured to execute the instruction, a digital image capture device in communication with the processor configured to capture a digital image of the scene, the processor configured to execute an instruction to generate a plurality of two dimensional digital images of the scene from said first two dimensional digital image of the scene via a camera angle rotation of between 1-180 degrees of said first two dimensional digital image of the scene for each of said plurality of two dimensional digital image of the scene as a sequence, and a display in communication with the processor, the display configured to display a multidimensional digital image sequence of an event.

This invention relates to the field of digital image processing technology, and discloses an intelligent adaptive reconstruction method, system, and apparatus for cross-scene image quality enhancement. The method generates an illumination intensity map based on paired supervision of a source domain image and a clear daytime target reference image, maps it to brightness-guided attention, and obtains a scaled illumination attention map after scaling. Brightness is then enhanced and fused, and an image enhancement mask is obtained through differential calculation. The enhanced reconstructed image is obtained by residual superposition reconstruction. A reference domain statistical template and a preset statistical threshold interval are constructed, distribution differences are calculated, and a parameterized mapping output domain alignment enhancement model is iteratively updated. During the inference phase, brightness remapping, contrast and detail gain upper limits, and key regions are automatically set, outputting an enhanced image whose brightness and color distribution are closer to the target reference domain. This invention can output enhanced images close to clear daytime images under various cross-scene conditions.

The invention relates to a calibration device and a calibration method of a low-altitude target early warning detection system, and belongs to the technical field of equipment calibration, the calibration device of the low-altitude target early warning detection system comprises a visible light sighting telescope and an electronic eyepiece which are fixed on radar equipment, the electronic eyepiece is arranged at the exit pupil position of the visible light sighting telescope, and the electronic eyepiece is connected with the visible light sighting telescope. The image acquisition module is used for acquiring analog images acquired by the visible light sighting telescope based on the eyepiece and converting the analog images into digital images; the processor is in communication connection with the electronic eyepiece and is used for acquiring the azimuth angle of the first servo motor; the pitching angle of the visible light sighting telescope is controlled to the target angle; acquiring an azimuth angle of a second servo motor; and based on the azimuth angle of the first servo motor, the azimuth angle of the second servo motor and the target angle, carrying out space consistency calibration on the radar equipment and the photoelectric equipment. According to the invention, the calibration complexity of the low-altitude target early warning detection system is effectively reduced, and the calibration efficiency is improved.

The embodiment of the invention relates to the technical field of digital image processing and computer vision, and provides a single image defogging method based on iterative transmissivity correction and a related device, and the method comprises the steps: obtaining a to-be-processed foggy image; performing atmospheric light value estimation processing according to the to-be-processed foggy image to obtain a global atmospheric light value; on the basis of dark channel prior, according to the to-be-processed foggy image, transmissivity graph calculation processing is carried out, and an initial transmissivity graph is obtained; according to the to-be-processed foggy image and the global atmospheric light value, performing optimization iteration processing on the initial transmissivity graph to obtain a target transmissivity graph; based on the atmospheric scattering model, image defogging processing is carried out according to the to-be-processed foggy image, the global atmospheric light value and the target transmittance graph, so that a more accurate target restored image can be obtained, and the defogging processing accuracy of a single image can be effectively improved.

This invention provides a novel raw domain denoising method and system, belonging to the field of digital image processing technology. The method includes: obtaining normalized raw image data and corresponding noise intensity parameters; determining the filter kernel size parameters corresponding to each pixel position, and simultaneously performing multi-directional structural analysis on the normalized raw image data to generate direction parameters and direction confidence parameters; constructing a direction-adaptive filter kernel, and performing filtering processing on the normalized raw image data to generate low-frequency denoised image data; calculating high-frequency residual information and determining high-frequency compensation parameters; performing high-frequency component compensation on the low-frequency denoised image data to generate denoised raw image data, and performing inverse normalization to output the raw domain denoising result. This invention achieves adaptive adjustment of the noise suppression process by combining noise intensity modeling and multi-directional structural perception within the raw domain, maintaining the stability of image structural information while ensuring effective noise suppression.