210 results about "Image Artifact" patented technology

Systems and techniques are provided for automatically analyzing and processing domain-specific image artifacts and document images. A process can include training a domain-adapted ML network using a domain-specific training dataset including a plurality of training data inputs corresponding to a lexicon of domain-specific terminology. A first fine-tuning training of the domain-adapted ML network yields a domain-adapted general QA ML network, based on using a first question answering (QA) dataset comprising a first plurality of question-answer training pairs that do not correspond to the lexicon of domain-specific terminology. A second fine-tuning training of the domain-adapted general QA ML network yields a fine-tuned domain-adapted general QA ML network, and uses a second QA dataset comprising a second plurality of question-answer pairs generated based on a corpus of text narratives utilizing the lexicon of the domain-specific terminology.

Systems and techniques are provided for automatically analyzing and processing domain-specific image artifacts and document images. A process can include obtaining a plurality of document images comprising visual representations of structured text. An OCR-free machine learning model can be trained to automatically extract text data values from different types or classes of document image, based on using a corresponding region of interest (ROI) template corresponding to the structure of the document image type for at least initial rounds of annotations and training. The extracted information included in an inference prediction of the trained OCR-free machine learning model can be reviewed and validated or corrected correspondingly before being written to a database for use by one or more downstream analytical tasks.

The invention relates to the technical field of image processing, and provides an image artifact detection method and system based on a deep learning technology, and the method comprises the following steps: carrying out the preprocessing of an obtained to-be-processed image, and carrying out the feature extraction of the preprocessed image; position coding is carried out on the extracted feature map A, and the extracted feature map A is fused with the feature map A; performing multi-layer encoding and decoding on the fused feature map through an encoder and a decoder to obtain a feature map B containing an artifact target area; and generating an anchor frame of an artifact region for a target region in the feature map B, and dynamically adjusting the size and position of the anchor frame according to local information of the down-sampling rate, texture complexity and edge strength of the feature map B to obtain an artifact region detection result after the anchor frame is labeled. Automatic artifact detection and quantitative evaluation of the influence degree can be achieved, the accuracy and efficiency of medical image interpretation are improved, and more reliable data support is provided for clinical decision making.

The invention belongs to the technical field of image processing, and particularly relates to a motion artifact adaptive correction method based on multi-modal MRI fusion, which comprises the following steps: acquiring sMRI and fMRI of a patient, preprocessing the sMRI and fMRI, creating a cross-modal feature fusion model, and inputting the preprocessed sMRI and fMRI into the cross-modal feature fusion model for double-branch feature extraction; the cross-modal feature fusion model carries out cross-modal feature interaction fusion on the extracted double-branch features and carries out multi-scale feature reconstruction; detecting image artifacts based on the reconstructed multi-scale features, and performing dynamic space-time attention correction on the detected image artifacts; and establishing a three-level loss function system to carry out multi-task joint optimization. By integrating sMRI and fMRI information and based on image processing and deep learning, the defect that various complex head movement conditions cannot be completely handled in the prior art is overcome, and the technical problems of information loss between image modalities and insufficient feature utilization are solved.

The invention discloses an image artifact restoration method and system based on an anti-fact diffusion model, and relates to the technical field of image processing, and the method comprises the steps: obtaining multi-modal data, and carrying out the data screening of the multi-modal data; preprocessing the screened data, and constructing a standardized data set; constructing an image artifact repairing model based on an anti-fact diffusion model; training the image artifact repair model through a standardized data set; and carrying out artifact restoration on the image polluted by the artifact through the trained image artifact restoration model. Only artifact-free data is used in the training stage, and the limitation of the lack of paired data on the current artifact repair technology is solved; the method is not limited to a single part of a single mode during restoration, and when images of different modes are processed, the quality of the restored images can still be ensured, and the high resolution and details of the images are kept; the image quality can be effectively improved, key details in the image are reserved, and the accuracy of the image is improved.

Systems and techniques are provided for automatically analyzing and processing domain-specific image artifacts and document images. A process can include obtaining a plurality of document images comprising visual representations of structured text. An OCR-free machine learning model can be trained to automatically extract text data values from different types or classes of document image, based on using a corresponding region of interest (ROI) template corresponding to the structure of the document image type for at least initial rounds of annotations and training. The extracted information included in an inference prediction of the trained OCR-free machine learning model can be reviewed and validated or corrected correspondingly before being written to a database for use by one or more downstream analytical tasks.

The invention discloses an image processing system and method for distinguishing xerophthalmia types, and relates to the technical field of image processing, and the method comprises the steps: firstly carrying out the standardization preprocessing of an original infrared meibomian gland image, further introducing an artifact perception link, and actively recognizing and positioning the interference regions, such as eyelash shielding and uneven illumination, in the image; and guiding the subsequent meibomian gland segmentation process by using the sensed artifact information, thereby realizing the accurate extraction of the meibomian gland form under the complex background. On the basis, multi-dimensional quantification is carried out on the precisely segmented gland form, and image features are converted into objective numerical indexes. And finally, carrying out comprehensive judgment on the numerical indexes by utilizing a decision tree model, and outputting standardized MGD severity grade. In this way, interference information brought by image artifacts can be intelligently suppressed, and therefore more robust and accurate MGD severity grading is achieved in a clinical image with poor quality.

The invention discloses an X-ray image artifact definition automatic correction and enhancement method based on deep learning, and belongs to the technical field of image artifact correction, and the method specifically comprises the steps: obtaining an X-ray image, employing frequency domain decomposition and edge detection to generate an artifact image and a sharpness image, and extracting an imaging parameter set and an anatomical region label; establishing a parameter-driven artifact migration network, inputting an imaging parameter set and an artifact graph, learning an artifact vector field and phase prior, and forming reversible representation of an artifact source; constructing a double-branch decoder, dissecting branches to generate a structural skeleton diagram, and performing cross attention fusion to obtain a candidate correction field; according to the candidate correction field, geometric remapping and sub-band compensation are carried out on the image to obtain an intermediate correction result image, and an artifact vector field is sent to a feedback loop; and the consistency of the sharpness graph and the structural skeleton graph is taken as a loss item, artifact residual constraint is combined, a correction field and decoder parameters are optimized, and non-anatomical textures are suppressed.

The invention discloses a medical image artifact identification and elimination method based on a big data technology, and relates to the technical field of medical image processing, and the method comprises the steps: carrying out the preprocessing of collected image data based on gray normalization, and then carrying out the semantic segmentation and ROI positioning of the image content; and performing lesion segmentation on the positioned image content, selecting lesion features for quantification and fusion, performing model verification, and performing distributed deployment on the verified lesion segmentation model. According to the method, the problems of high missed detection rate of small nodules and high missed diagnosis risk of malignant lesions are solved through the lesion detection model, the false positive rate is reduced, the recall rate of the malignant lesions is improved, and through the lesion segmentation model, the segmentation adaptability to lesions of different sizes is improved, clear segmentation boundaries are obtained, surgical planning is assisted, and boundary positioning errors are reduced.

The invention relates to a preprocessing method for an infrared focal plane image sequence, which comprises the following steps of: averaging each pixel point of an image in a time domain to obtain a time domain mean value; then local nonlinear filtering is used, the maximum value and the minimum value in a window are removed in the sliding window of the image, dynamic threshold detection is carried out on the time domain mean value of each point, the local mean value of the window and the maximum value and the minimum value in the window, and pixels exceeding the threshold are marked as blind pixels; after median filtering compensation is carried out on blind pixels, a time-space domain difference operator non-uniformity correction model is established for an infrared image sequence, and the infrared image sequence is solved by using an alternating direction operator method, so that the infrared image sequence is pre-processed in a self-adaptive manner. When the method is used for preprocessing, the accuracy of blind pixel compensation is high, the non-uniformity noise correction effect is good, and image artifacts are effectively reduced.

The invention discloses a low-orbit image artifact rapid elimination method based on a pulse coupling neural network. The method comprises the following steps: S1, acquiring original low-orbit image data; s2, inputting the original low-orbit image data into a satellite-borne hierarchical progressive pulse coupling neural network model; s3, forming an artifact candidate region, and generating an artifact probability graph based on the synchronization pulse connectivity and the information entropy increment; s4, generating preprocessed low-orbit image data, and synchronously generating a reversible repair log; s5, generating an artifact-rejected high-quality image product; and S6, updating the coupling gain coefficient and the link strength threshold of the spaceborne hierarchical progressive pulse coupling neural network model according to the processing state of the artifact-rejected high-quality image product. The system is stable in closed-loop operation, the image quality is remarkably improved, the processing delay meets the quick remote sensing service requirement, and the engineering feasibility is achieved.

Disclosed herein is a medical system (100, 300) comprising a memory (110) storing machine executable instructions (116) and a super resolution neural network (118). The super resolution neural network is configured to receive an initial magnetic resonance image (114, 114′) descriptive of a subject (318), having a first resolution, and containing an image distortion artifact. The image distortion artifact is a Gibbs ringing image artifact. The super resolution neural network is configured to output an enhanced magnetic resonance image in response to receiving the initial magnetic resonance image. The enhanced magnetic resonance image has a second resolution, that is higher than the first resolution, and has a reduction or removal of the image distortion artifact. Execution of the machine executable instructions causes a computational system (104) to: receive (200) the initial magnetic resonance image and receive (204) the enhanced magnetic resonance image in response to inputting the initial magnetic resonance image in to the super resolution neural network.

The invention provides a radiographic inspection real-time quality inspection system and device based on artificial intelligence and cloud edge collaboration, and relates to the technical field of radiographic inspection quality inspection.The system mainly comprises a data acquisition and preprocessing module, an AI analysis module and an early warning response and feedback module which are sequentially arranged; the data acquisition and preprocessing module comprises a medical image unit, a hospital information unit, an equipment parameter unit and a data association unit; the AI analysis module comprises a medical image recognition unit, an anatomical feature and left and right identifier analysis unit and a data fusion comparison and risk assessment unit. According to the scheme, abnormal conditions such as mismatching between the inspection application and the actual operation and image artifacts can be recognized and early warned in an extremely early stage, so that the probability of operation errors is remarkably reduced, medical quality accidents are effectively avoided, and the standardization and safety of radiation inspection are improved.

A method comprising: receiving 4D-tomographic image data, said 4D-tomographic image data including a plurality of 3D-tomographic image data of an examination object, and the plurality of 3D-tomographic image data corresponding to a plurality of time points; applying a segmentation algorithm to the 3D-tomographic image data, wherein the segmentation algorithm is configured to segment at least one organ in the 3D-tomographic image data to which the algorithm is applied; applying a scoring function to the segmented organ(s), wherein the scoring function is configured to determine a scoring value(s) for the segmented organ(s), and wherein the scoring value includes and / or corresponds to a metric quantifying an extent to which a vicinity of voxels at a surface of the segmented organ(s) contain(s) an image artifact; comparing the scoring value(s) with a threshold value; and providing a user notification, when at least one scoring value exceeds the threshold value.

The invention discloses an underwater image enhancement method based on space-frequency feature interaction, and the method achieves the high-quality enhancement of an underwater image through the deep learning technology. The underwater image enhancement network mainly comprises an amplitude sub-network, a phase sub-network and a degradation feature extraction module, and a space-frequency feature interaction module is introduced. The amplitude sub-network is used for improving color and contrast features of an underwater image, the phase sub-network is used for recovering texture details and structure information of the image, and the degradation feature extraction module is used for pertinently guiding an enhancement process of degradation features. The space-frequency feature interaction module realizes complementary learning of global and local features, and solves the problem of lack of global consistency caused by convolution operation locality. Besides, a special frequency domain loss function is designed in the method, frequency components are constrained, and image artifacts caused by frequency deviation are suppressed.

The invention relates to a medical image artifact classification method based on a CN-CLIP model, and the method comprises the steps: obtaining a medical image data set containing a plurality of CT images and MR images, and constructing a text label data set in a multi-segment cue word format based on the image type, examination part and artifact type of each image; performing fine tuning on the CN-CLIP pre-training model by using the medical image data set and the text label data set to obtain an artifact classification model; and inputting a medical image to be classified into the artifact classification model, and outputting an artifact classification result of the medical image. According to the medical image artifact classification method based on the CN-CLIP model, the pre-trained Chinese image-text matching model CN-CLIP is utilized, so that the defect that models need to be trained respectively for different image types and examination parts in the prior art is overcome; efficient and accurate artifact classification of CT and MR medical images of different parts is realized through a unified model.

A method includes obtaining emission-tomography functional image data and a corresponding reconstructed anatomical image volume including at least one organ having natural motion; pre-determining a dedicated model for spatial mismatch correction of the at least one organ; performing initial image reconstruction of the emission-tomography functional image data to generate a reconstructed emission-tomography functional image volume utilizing attenuation correction based on the corresponding reconstructed anatomical image volume; and identifying relevant anatomical regions, within both image volumes, where functional image quality may be affected by the natural motion of the at least one organ. The method includes identifying and evaluating potential attenuation-correction image artifacts in the reconstructed emission-tomography functional image volume; estimating model parameters based on confirmed attenuation-correction image artifacts; correcting the corresponding reconstructed anatomical image volume to generate a corrected anatomical image volume; and reconstructing the emission-tomography functional image data utilizing attenuation correction based on the corrected anatomical image volume.

The invention belongs to the technical field of image processing, and particularly relates to an efficient compressed image artifact removal method based on multi-feature fusion, which comprises the following steps of: preparing data; constructing a shallow feature extraction module; constructing a multi-scale feature extraction module; constructing an image restoration module; constructing a loss function L; and moving the constructed network model to a GPU for training, optimizing the image recovery network by adopting an Adam optimizer, calculating a loss function and then reversely propagating the loss function to the network for optimization, stopping training when the loss function converges, and after the training is completed, storing a model.pth weight file for subsequent test use. The multi-scale feature extraction module can capture artifact features of different scales through cascade design of a recursive residual unit (RRU) in combination with residual connection and multi-scale feature multiplexing, and the characterization capability of the model for complex artifacts is enhanced. In addition, the recursive structure reduces the calculation complexity through parameter sharing, and improves the model efficiency.

The invention provides an image artifact recognition method, an image artifact recognition device, a terminal and a computer storage medium, and the method comprises the steps: carrying out the preprocessing of artifact enhancement of a to-be-processed image, so as to obtain an artifact-enhanced image; based on a preset artifact identification model, performing artifact preliminary identification on the artifact enhanced image to obtain each artifact area in the artifact enhanced image; for a single artifact region, extracting a corresponding maximum connected domain, and performing spatial feature extraction on the maximum connected domain to obtain a sub-artifact recognition result of the corresponding artifact region; and obtaining a total artifact identification result of the to-be-processed image based on the sub-artifact identification results of all the artifact areas, thereby accurately and efficiently identifying the artifact areas on the to-be-processed image so as to facilitate the proceeding of a subsequent artifact elimination step.

A method includes obtaining, using at least one under display camera, one or more first image frames associated with a first diffraction pattern and one or more second image frames associated with a second diffraction pattern. The first diffraction pattern and the second diffraction pattern are related through a transformation. The method also includes generating a first deblurred image using the one or more first image frames and a second deblurred image using the one or more second image frames. The method further includes combining the first and second deblurred images while exploiting complementary types of image artifacts created by the first and second diffraction patterns to generate an image of a scene.

The application discloses a TDI-CCD image deartifact method and device, equipment, medium and product, and relates to the technical field of image processing. The method comprises the following steps: constructing a deep deblurring network; the deep deblurring network comprises a Wiener filter module and a generative adversarial network, and the Wiener filter module is arranged at the input end of a generator of the generative adversarial network; the Wiener filter module is used for filtering a frequency domain image; the deep deblurring network is trained, and the trained deep deblurring network is used as a deartifact model; during the training of the deep deblurring network, a point spread function and a regularization parameter of the Wiener filter module are used as learnable network parameters; and an image with row smear obtained from a TDI-CCD camera is processed by using the deartifact model to obtain a deartifact image. The application can effectively remove image artifacts and improve the definition of a TDI-CCD image.

The application relates to a mode filter, a topology optimization method and an ultrasonic guided wave damage imaging artifact suppression method, relates to the field of ultrasonic detection and imaging, and the topology optimization method comprises the following steps: acquiring initial design parameters, generating an initial population, each chromosome individual in the initial population is coded by a binary logic matrix representing material distribution and meeting design constraints; a population is optimized by using a genetic algorithm, an adaptability function in the genetic algorithm is constructed based on a mode purity index and an energy transmission index, the mode purity index is the ratio of in-plane displacement integral in a transmission area in finite element simulation with the mode filter to total displacement integral, and the energy transmission index is the ratio of relative energy at a transmission end in the finite element simulation with the mode filter to relative energy of an S0 mode; and final optimized material distribution of the mode filter is output. Compared with the prior art, the application has the advantages of effectively suppressing background artifacts, giving consideration to mode regulation performance and substrate structure integrity and the like.

Techniques for generating for training a machine learning model to detect image artifacts include training, based on a first plurality of video frames having synthetic artifacts, a machine learning model to generate a trained machine learning model, generating, based on a second plurality of video frames, a plurality of first artifact detections using the trained machine learning model, selecting, from the second plurality of video frames based on the plurality of first artifact detections, to generate refinement data, and re-training, based on the first plurality of video frames and the refinement data, the trained machine learning model to detect image artifacts in video frames.

The invention relates to CBCT integrated equipment carrying an injection pump and an application method and application device of the CBCT integrated equipment. According to the method, a bed body, a ray generating device, a detector, an injection pump and an infusion tube are included, and a tube groove is formed in a non-imaging area of a bed board so as to fix the infusion tube, avoid the winding phenomenon of the infusion tube, guarantee normal infusion and further reduce image artifacts; the mechanical driving device can drive the pump body to rotate under the condition that medicine needs to be injected, so that a needle head part of the pump body points to the position lower than the horizontal plane, and outflow of the medicine to be injected is guaranteed; the mechanical driving device can drive the pump body to rotate under the condition that medicine does not need to be injected, so that the needle head part of the pump body points to the position higher than the horizontal plane, and the medicine to be injected is prevented from flowing out of the pump body. Furthermore, the CBCT integrated equipment carrying the injection pump can directly carry out image exposure after injection so as to timely and accurately capture three-stage contrast images and obtain high-quality images.

The utility model provides an X-ray detector and an imaging system. According to the X-ray detector, the positioning columns are matched with the positioning holes of the positioning sleeves fixed to the circuit board assembly, so that the flicker module and the photoelectric conversion module of the circuit board assembly are positioned and aligned more accurately. Moreover, the structure of the existing circuit board assembly does not need to be greatly changed, and the functions can be realized only by using the additionally arranged positioning sleeve with smaller processing error, so that the cost for developing a new circuit board assembly is saved. In the imaging system, more accurate positioning and alignment are realized by the flicker module and the photoelectric conversion module of the circuit board assembly, so that the signal-to-noise ratio of the X-ray detector for imaging can be improved, the risk of image artifacts is reduced, and better imaging quality is obtained.

The invention relates to the technical field of reconstructed image anomaly detection, in particular to an image data anomaly point detection and correction method and system based on magnetic resonance fingerprints, and the method comprises the steps: generating a simulation data dictionary containing random anomaly points based on a magnetic resonance scanning plane; obtaining a to-be-detected reconstructed image, extracting a reconstructed image signal evolution curve, and matching the reconstructed image signal evolution curve with the simulation data dictionary to obtain an abnormal point position in the to-be-detected reconstructed image; and correcting the image data according to the positions of the abnormal points in the to-be-detected reconstructed image to obtain a corrected image. According to the method, the scanned and acquired data is matched with the pre-generated data dictionary containing the random abnormal points, so that whether the abnormal points are contained or not can be detected, the abnormal points are corrected, related image artifacts are eliminated, reconstructed image anomaly detection can be realized without an AI model, manpower and material resources are saved, the stability is better, and the detection efficiency is improved. Deployment and implementation can be carried out in magnetic resonance imaging equipment.

The application provides an automatic artifact detection and repair method and system based on an endoscope image, constructs an artifact removal model, acquires an original endoscope image, pre-processes the original endoscope image, extracts features of the original endoscope image through a feature pyramid encoder, combines an attention gate module to perform feature screening and feature fusion, acquires an endoscope feature map, inputs the endoscope feature map into a multi-branch decoder, outputs a multi-branch image, reconstructs the multi-branch image through a differentiable rendering layer, acquires a reconstructed endoscope image, acquires a constraint loss according to a multi-level perception constraint mechanism, adjusts parameters of the artifact removal model according to the constraint loss, performs detail enhancement on an intrinsic image to acquire an enhanced image, scores and screens the enhanced image, and acquires a repaired image. Through the technical solution with the multi-artifact decoupling repair function, the accuracy of endoscope image artifact repair is improved.

Deep learning algorithms are applied to electrocardiogram (ECG) traces to more accurately detect, locate, and / or predict true R-waves within the ECG traces. Accurate R-waves may be used to reduce image artifacts due to ECG gating or arrhythmia in real-time imaging. Corrected R-waves may be used for retrospective image reconstruction of cine cardiac MRI by re-binning spatial frequency maps to produce corrected cine MRI with reduced artifacts and uncovered cardiac anatomy.

The invention relates to the technical field of medical images, in particular to a medical image processing auxiliary device and method, and the method comprises the steps: S1, height adjustment: adjusting the height of an examination plate according to the height of a patient; s2, fixing elbows: placing the elbows of the patient in a fixing box and fixing the elbows; s3, adjusting the body position: controlling a first electric control cylinder to drive a fixing box to move towards one side close to the examination plate by a control module, identifying the distance between a connecting block and the examination plate through a laser ranging unit, and further controlling the moving distance of the fixing box; s4, illumination imaging: after the thoracic cavity is attached to the examination plate, illumination imaging is carried out; and S5, image processing: converting the information acquired by the image acquisition module into a clear image, displaying the image on a man-machine interaction screen in real time, and transmitting the image to a data storage and communication module for storage and synchronization after the preview image is confirmed to be correct. The elbow of the patient is attached to the examination plate, so that the thoracic cavity of the patient is attached to the examination plate, and image artifacts are avoided.

The application discloses a work robot screen cabinet target detection method and system, comprising: preprocessing a screen cabinet image; constructing a bidirectional network, which is trained based on a loss function comprising a screen cabinet target shape loss and a screen cabinet target position loss; using a first branch to sequentially extract feature maps from the preprocessed screen cabinet image, using a second branch to perform three-layer convolution operation on the preprocessed screen cabinet image with a stride of two to obtain 1 / 8 feature maps; performing first feature fusion on the 1 / 8 feature maps of the first branch and the 1 / 8 feature maps of the second branch, performing second feature fusion on the obtained fusion 1 / 8 feature maps and 1 / 32 feature maps to obtain a to-be-recognized feature map, and obtaining a detection result of the screen cabinet target according to the to-be-recognized feature map. The method avoids the interference of image artifacts or noise, reduces target detection error, has strong anti-interference ability and high recognition accuracy.