11 results about "Streaking Artifact" patented technology

The invention discloses a tunnel cross-sectional image analysis method based on image processing, and aims to solve the problems that a surface image is not clear in correspondence with a transient seismic wave method, a geological radar and a resistivity imaging section space, and anomalies at a certain distance in front are difficult to map to a tunnel face. According to the method, anisotropic reforming is carried out by adopting a Fourier neural operator, cross-modal Transform registration of a micro attention mask based on sector geometry, curve mileage and section polar coordinate position coding is combined, analytic geometry mapping and uncertainty propagation are matched, tunnel face structure traces and water seepage texture evidences are fused, and the tunnel face structure traces and the water seepage texture evidences are combined. The technical effects of accurate positioning of abnormity on the tunnel face, position confidence range estimation, risk grading early warning, stripe artifact suppression, abnormal boundary reservation and output of structured results of mileage stake numbers, azimuth angles, distance intervals and the like are achieved.

The invention discloses an ultra-sparse CBCT (cone beam computed tomography) reconstruction method, system and equipment based on an ordinary differential equation, belongs to CBCT reconstruction in the field of artificial intelligence, and aims to solve the technical problem of low quality of CBCT reconstructed images. The method comprises the following steps: acquiring sample data, preprocessing the data, constructing and training a CBCT-CT nonlinear relation reconstruction model, and performing real-time reconstruction; during preprocessing, converting the three-dimensional image volume data into simulated X-ray projection data, and reconstructing the simulated X-ray projection data by adopting an FDK reconstruction algorithm to obtain an FDK-CBCT image; the CBCT-CT nonlinear relation reconstruction model comprises an encoder, a NODE module and a decoder; in the training process, the CBCT-CT nonlinear relation reconstruction model is trained through the obtained CT sample image and the FDK-CBCT image. In the reconstruction model, through continuous evolution of NODE modeling image features, the model can model a continuous evolution mapping process from a sparse low-quality image to a high-quality CT image during training, so that stripe artifacts and structural distortion do not easily exist in the reconstructed image, and the reconstruction quality is high.

A sparse helical CT image reconstruction method based on a differentiable helical reconstruction operator. First, actual helical scanning geometric parameters of a subject and corresponding full-angle helical projection data are acquired, and a final reconstructed image is acquired by means of seven steps. In the present invention, actual scanning geometry is used to perform forward projection on a reconstructed sparse-angle image, thereby providing geometric prior guidance for missing projections; moreover, on the basis of the similarity and redundancy characteristics of adjacent projections in helical scanning, a projection completion network is constructed, and by learning bidirectional motion fields of adjacent angles and in combination with geometric prior projections, intermediate missing projection data is jointly synthesized; in addition, the global streak artifact restoration of the image is realized; and finally, the joint training of a projection domain and an image domain is realized, thereby facilitating integral restoration by using projection-image dual-domain information, and data collected within two pitches is used for restoration, thereby effectively avoiding an excessive computational load.

The present application provides a kind of night image halo removal method, including constructing prior knowledge, according to prior knowledge, construct initialization network and depth unfolding network, using initialization network to extract variable initial value set from night halo pollution image;Night halo pollution image and variable initial value set are input into depth unfolding network and are operated multiple iterations, and the final halo removal image is extracted from night halo pollution image;Wherein, depth unfolding network includes multiple proximal networks, and each proximal network participates in an iteration operation.The present application makes full use of prior knowledge, uses the depth unfolding network with multiple proximal networks to iterate mapping diagram, halo-free feature map, constraint variable and halo removal image, so as to extract the final halo removal image, to better preserve image texture and restore image details, under the premise of not increasing application cost, the halo and stripe artifact in image are preferably removed.

The invention discloses a method for removing flicker stripes of a rolling shutter camera based on a self-supervision mechanism. The method comprises the following steps: S1, generating a rolling shutter image with a flicker effect; s2, acquiring two training images containing flickering stripes in the same scene; s3, inputting the first flicker image into a neural network model to obtain a prediction output image; s4, the second flicker image is used for processing, and a supervision signal serving as a prediction output image is generated; s5, calculating the loss between the prediction output image and the supervision signal; and S6, according to the loss, adjusting parameters of the neural network model by using a back propagation algorithm based on gradient descent. According to the method provided by the invention, the stroboscopic stripes on the picture can be removed, and the stripe-free picture is output; the problem that fringes are not thoroughly removed due to the fact that an existing non-supervision method lacks clear supervision signals is solved. The problem that a general loss function is insufficient in specific stripe artifact suppression capability is solved.

This paper relates to the field of image processing, and more particularly to a method and apparatus for eliminating stripe artifacts in light-sheet fluorescence microscopy images. The method includes: acquiring multiple striped images obtained by scanning a biological tissue sample at the same location from multiple angles using a light-sheet fluorescence microscope; constructing a stripe-free image based on the multiple striped images; using the multiple striped images and their corresponding stripe-free images as training datasets; training a deep adversarial network (DAN) based on the training datasets; and using the trained DAN to eliminate stripe artifacts in the striped images of the target biological tissue sample. The embodiments described in this paper eliminate stripe artifacts in striped images of biological tissue samples obtained by light-sheet fluorescence microscopy by training a DAN model, improving the clarity of the stripe-free image obtained after stripe artifact removal. Furthermore, this method is applicable to various light-sheet fluorescence microscopy systems, improving the adaptability of the stripe artifact removal method.

The application discloses a tunnel fault image analysis method based on image processing, and aims at solving the problems that the surface image cannot be corresponded with the spatial profile of the transient seismic wave method, the geological radar and the resistivity imaging profile, and the abnormality in a certain distance in front cannot be mapped to the working face, wherein the anisotropic regularization is performed through the Fourier neural operator, the cross-modal Transformer registration based on the differentiable attention mask and the curve mileage and the cross-section polar coordinate position coding of the sector geometry is combined, the analytical geometry mapping and the uncertainty propagation are matched, the working face structure trace and the water seepage texture evidence are fused, the accurate positioning of the abnormality in the working face, the position confidence range estimation and the risk grading early warning are realized, the stripe artifact is inhibited, the abnormal boundary is reserved, and the technical effects of outputting the structured results such as the mileage post number, the azimuth angle and the distance interval are achieved.

The application discloses a high-speed CT intelligent image reconstruction method for transient detection, which is applied to a distributed multi-source transient CT imaging system, acquires sparse angle projection data of multiple fixed X-ray sources under different projection angles, and constructs a linear imaging system equation; an accelerated random differential equation framework is constructed, multi-scale dynamic modeling is carried out by introducing an integer scale index, and time resolution and noise intensity are decoupled; a quasi-equivalent Markov chain solver is used for image reconstruction sampling, and the sampling process is sequentially divided into a non-Markov bridge stage, a link path stage and a Markov bridge stage; after each sampling step, a data consistency constraint is applied to correct the sampling state, and a reconstructed image is obtained; and the application effectively suppresses stripe artifacts under the condition of extremely sparse viewing angles by means of multi-scale decoupling and a three-stage sampling strategy, and ensures the image reconstruction quality.

This invention discloses a method, system, medium, and device for NNBI visual tomographic image reconstruction, relating to the field of neutral beam imaging technology using negative ion sources. The NNBI visual tomographic image reconstruction method and system provided by this invention utilizes a tomographic reconstruction algorithm to invert the observed projection map of an NNBI multi-angle beam image, obtaining an initial reconstructed image and constructing its ROI mask. Then, the mask is input into a pre-trained residual denoising network via two channels to acquire the residual image and calculate the denoised image. This suppresses interference from background and noise outside the ROI and improves the stability of denoising, thereby improving image reconstruction efficiency and image quality. Furthermore, based on a forward projection operator constructed using camera calibration parameters and the observed projection vector, the reprojection residual of the denoised image is calculated and a correction value is generated. This correction value is used to perform data consistency correction on the denoised image to obtain the target reconstructed image, maintaining projection consistency and preventing consistency degradation caused by depth denoising. This invention effectively suppresses isolated points and stripe artifacts during image reconstruction, improving the quality of image reconstruction.

A phase contrast X-ray imaging system includes an X-ray source, a plurality of gratings, a detector for detecting X-rays, a grating movement mechanism, and a controller. The controller generates a phase contrast image based on intensity changes that represent changes in pixel values of pixels detected by the detector while moving a scanning grating, which is at least one of the plurality of gratings, using the grating movement mechanism. The controller acquires an analysis period representing a period of the intensity change to reduce a moire artifact, and adjusts a set value of movement of the scanning grating for generating the phase contrast image based on the analysis period acquired.

The invention belongs to the technical field of X-ray detection, and provides an X-ray image processing method and device based on deep learning, and the method comprises the steps: recognizing a fog scattering region in an X-ray image; performing stripe artifact analysis under coupling combination of different jig vibration frequencies and X-ray pulse frequencies, and identifying a high-probability vibration-pulse frequency group with stripe artifacts; comparing and judging whether the X-ray image has a periodic stripe artifact risk or not; if the X-ray image exists, determining the direction and spacing of the stripe artifacts through gray gradient analysis of the X-ray image, and marking a stripe artifact coverage area; according to the method, a scattering artifact overlapping area is identified through overlapping analysis of a foggy scattering area and a stripe artifact coverage area, regional artifact removal is carried out through a deep learning network, a global artifact-removed image is output in combination with multi-area weight fusion, and the detection precision is improved.