63results about How to "Improve reconstructed image quality" patented technology

The invention discloses a method for three-dimensional representation of stratified structure micrometer CT imaging of a turbine blade heat barrier coating. The method comprises the following steps: preparing a double-layer-structure heat barrier coating micrometer CT scanning sample; firstly, carrying out micrometer CT scanning on the heat barrier coating sample, carrying out ring artifact and beam hardening correction on projection data, then carrying out three-dimensional reconstruction to obtain three-dimensional stratified structure information, establishing a three-dimensional structure analysis model, and carrying out characteristic segmentation and extraction on three components, namely a ceramic heat insulating layer, a bonding layer and matrix alloy; then, analyzing the thickness of the coating, extracting the distribution of holes in the coating, calculating the porosity, extracting the interface topography of the coating, and analyzing structure characteristics such as internal defects; and finally, estimating the preparation process quality of the heat barrier coating by integrating the composition, thickness, porosity distribution, interface topography and the like. The method for three-dimensional representation of the stratified structure of the heat barrier coating provided by the invention is suitable for estimation and optimization of the spraying process quality of the heat barrier coating, so that the quality of the heat barrier coating can be well controlled.

The invention relates to a compressed sensing measurement matrix optimization method and system based on automatic encoder network. The method comprises the steps of obtaining original images as training data and dividing the training data into a plurality of image blocks through segment cutting processing; conducting sampling on the image blocks based on the preset sampling rate and the automaticencoder network, and generating initial reconstruction images; calculating the residual error value between the initial reconstruction images and the original images based on the deep residual errornetwork; combining the residual error value with the initial reconstruction image and generating a reconstruction result, and establishing a loss function based on the reconstruction images and the image blocks, and conducting training on the parameter in the automatic encoder network through the loss function, and using the automatic encoder network parameter after training completion as the compressed sensing measurement matrix. The invention is advantageous in that through the transformation of data dimensions via the automatic encoder, the process from collecting to reconstructing of images can be simulated and realized, wherein the parameter in the collecting process is the measurement matrix, which has good reconstruction quality.

The invention discloses a three-dimensional electrical capacitance tomography (ECT) imaging image reconstruction method which employs the measuring capacitance value obtained by a three-dimensional ECT imaging sensor as the projection data for image reconstruction. In an iteration reconstruction process, a threshold filtering mode is employed to perform artifact inhibition on an obtained reconstruction image; a filtering threshold regularly performs adaptive adjustment, and is selected to minimize the fuzzy measurement of a current image. The method effectively reduces reconstruction image artifacts, improves complexly distributed three-dimensional imaging effects, and realizes real three-dimensional ECT imaging image reconstruction.

The invention discloses a CT (computed tomography) machine. The CT machine comprises a bulb tube, a ray box, a collimator and at least one pair of reference detectors, wherein the collimator is positioned in the ray box; the reference detectors are arranged in the ray box; the reference detectors and the bulb tube are respectively positioned on two sides of the collimator along a ray exiting direction; and the reference detectors are positioned in a penumbra field of rays of the bulb tube. The invention also provides a real-time monitoring method for the focal point of the bulb tube of the CT machine. The reference detectors are arranged in the ray box of the CT machine, so that the size and the position of the focal point of the bulb tube can be simultaneously monitored. Compared with a method for only monitoring the position of the focal point, the real-time monitoring method has the advantages that the accurate data of the position and the size of the focal point of the bulb tube can be acquired, and the quality of reconstructed images is improved.

The invention belongs to the technical field of X ray computerized tomography (CT), and particularly relates to a wide visual field cone-beam X ray oblique scanning three-dimension digital imaging method based on an algebraic reconstruction algorithm. The method comprises the following steps: acquiring two-dimension projection data of a scanned component in detector biasing wide visual field cone-beam X ray oblique scanning way; and rebuilding an image by the algebraic reconstruction algorithm to obtain a three-dimension computerized tomography image in a scanning area. Compared with the widevisual field cone-beam X ray oblique scanning three-dimension digital imaging method based on a filter back projection algorithm, the method can effectively inhibit structure aliasing between layers and obviously improve the quality of the rebuilt picture in the condition that the system hardware, the scanning view field and the scanning speed are not changed.

The invention discloses a multi-frame low-resolution image super-resolution reconstruction method based on a convex combination mode, belongs to the field of computer vision, mainly solves a problem of image processing precision and provides an optimization algorithm. The method comprises the following steps of applying joint motion estimation and super-resolution reconstruction methods to an input multi-frame low-resolution (LR) image sequence to obtain a joint high-resolution (HR) image Ijoint; applying a reconstruction method based on improved kernel regression to the input multi-frame LR image sequence to obtain a kernel regression HR image Ikernel; and applying a convex combination frame provided by the invention and deciding a fused region according to an airspace fusion rule, deciding frequency components that need to be fused according to a frequency domain rule and deciding weight distribution of fusion of two methods according to the global weight, and then fusing the HR images obtained by the two steps through the three aspects to obtain a final reconstructed HR image.

The invention discloses an image coding method, an image coding device, an image decoding method and an image decoding device based on a super-resolution technology. The image coding device is positioned at a sending end of an image, and comprises a downsampling module used for performing downsampling on the original image; a first compression module used for compressing the downsampled image forthe first time; a neural network used for performing super-resolution reconstruction on the image that is compressed for the first time to acquire the image that is reconstructed for the first time; afirst addition module used for subtracting the image that is reconstructed for the first time from the original image to acquire a first residual image; a second compression module used for compressing the first residual image for the second time; and a sending module used for jointly sending the compressed first residual image and the image that is compressed for the first time to a receiving end of the image. According to the methods and devices provided by the invention, the code rate is greatly reduced, and meanwhile, the quality of the reconstructed image also can be well enhanced by theapplication of the deep learning technology.

The invention relates to a high-efficiency image collection and compression method. The method includes: adopting an image detector array to acquire a quantized digital image; utilizing a lower sampling module to acquire thumbnail data of an original image; acquiring data of the original data after dimension reduction through a compression sensing module; sequentially outputting the thumbnail data and compression sensing dimension-reduction data through a compressed data output module according to a preset compression ratio to form code stream used for transmission or storage; after the compressed data are stored and transmitted, completing reconstruction of the original image through an image reconstruction module. By the high-efficiency image collection and compression method, complexity of conventional image compression is lowered effectively, and the defect that a reconstruction step is needed to realize image looking-back on the basis of conventional compression sensing data is overcome.

The invention discloses a motion estimation and adaptive video reconstruction method based on interpolation. In the time super-resolution video reconstruction process based on compression perception,for a sample learning reconstruction algorithm, the close correlation between the video image reconstruction quality and training samples is considered, and adaptive classification reconstruction is carried out according to the object exercise amount. According to the method, firstly, a coded image is acquired through a pixel continuous equal-length exposure mode, secondly, the motion area is segmented through utilizing a method based on coded image scattering characteristics, initial video images are rapidly acquired through utilizing an interpolation method, the exercise amount of each areais estimated based on the initial images, and lastly, corresponding training parameters are selected according to different motion amount to carry out classification reconstruction. The method is advantaged in that the scene motion information is rapidly acquired before image reconstruction, and the video image reconstruction quality is further improved.

The invention provides a coincidence processing method of a scintillation pulse event. The method comprises the following steps: S1, acquiring experimental data of the PET system; wherein the experimental data comprises time information that a pair of gamma photons with the same energy and opposite directions generated after positron annihilation arrives at two corresponding detectors and energy information that the gamma photons deposit in the detectors; wherein the time information is Ti1 and Ti2 respectively; wherein the energy information is Ei1 and Ei2; s2, a step S2 is performed; settingan energy window and a time window and acquiring a coincidence event; energy window setting E, time window setting to T, wherein the gamma photons meeting the following conditions are marked as a pair of true coincidence events: |Ti1-Ti2|<=T, Ei1<=E, and Ei2<=E. The method comprises the following steps: S3, carrying out weighting processing on the true coincidence events, and carrying out enhancement processing by adopting two formulas, S4, carrying out image reconstruction to obtain an image result, and S5, analyzing the image result. The weights of the coincidence events are distributed byutilizing the energy information, the quality of the reconstructed image is improved, and the adaptability is high.

The invention discloses a support set and signal value detection based video compressive sensing reconstruction method which mainly solves the problem of poor reconstructed image quality in the prior art. The method includes the implementation steps: (1) dividing a video sequence into reference frames and non-reference frames according to image groups; (2) dividing the reference frames and non-reference frames into non-overlapping macro blocks identical in size; (3) subjecting all the macro blocks to compressive sensing measurement; (4) utilizing measurement values as input and updating iteration variables of a reconstructed image; (6) updating a support set and a signal detection value according to updated iteration variables of the reconstructed image; (7) computing a residual error of the reconstructed image according to the signal detection value; (8) judging whether iteration is terminated or not according to constraint conditions of the residual error of the reconstructed image; (9) outputting a reconstructed image signal. The support set and signal value detection based video compressive sensing reconstruction method can improve reconstructed image quality effectively and can be utilized for video image processing.

The invention discloses a CT image reconstruction method and system based on a convolutional neural network, and the method comprises the steps: inputting projection data, carrying out the filtering processing and back projection processing of the projection data through a front-end convolutional neural network, generating tomographic image information, and processing the generated tomographic image information through a back-end convolutional neural network. And calculating the value of the whole network loss function by using the processed tomographic image and the real image, reversely feeding back the gradient information of the loss function layer by layer, and updating the parameter value of each network layer. According to the invention, the projection data is processed by using the multi-layer convolutional neural network, useful information in the projection data is fully mined, and the feature extraction and information expression capabilities of the overall neural network model are effectively improved; a full connection layer is prevented from being used in the neural network model, and the required neural network model parameters are few and easy to implement.

The invention discloses a multimedia image compression method based on a non-correlation chaos observation matrix. The method comprises the steps that a video to be detected is read; under the condition that compression ratio is determined, observation frequency is determined; a Logistic mapping system is selected to produce a chaos factor; through the chaos factor, the first row of the observation matrix is determined, and a sequential cycle method is used to acquire vectors of other rows of the observation matrix; a non-correlation factor is introduced to strengthen the column non-correlation of the observation matrix; for each element moving from the tail end to the front end, the element is multiplied by the non-correlation factor; each time the element moves, the element is multiplied by the non-correlation factor in a superposed manner; and QR decomposition is used to process the observation matrix to acquire an orthogonal observation matrix based on non-correlation chaos. Compared with similar algorithms, the method provided by the invention has the advantages that a storage space restriction is made up; sufficient column non-correlation is met; a multimedia image is well compressed; the quality of a reconstructed image is great; the method is robust for different multimedia images; and the method is highly practical.

The invention relates to a CT imaging system and an imaging method, belongs to the technical field of radiation imaging, and solves the problem that an existing CT system adopting static CT equipment needs a large number of radiation sources and detectors. The CT imaging system comprises a static imaging subsystem, a dynamic imaging subsystem and a rack, the static imaging subsystem and the dynamic imaging subsystem are both fixed to the rack, and the static imaging subsystem comprises multiple sets of static imaging units which are fixedly arranged. By fusing the static imaging subsystem and the dynamic imaging subsystem, the arrangement number of ray sources and detectors in static CT can be reduced, relatively complete projection data can be obtained in a data fusion mode, the quality of reconstructed images is improved, and relatively complete and ideal overall reconstruction results are obtained.

The invention provides a linear scanning CL reconstruction method based on projection visual angle weighting. The method comprises the following specific steps: 1) data collection: carrying out the CL linear scanning of a to-be-measured object, and obtaining projection data; 2) performing image primary reconstruction: performing image reconstruction on the projection data collected in the step 1) by using a filtered back projection algorithm to obtain reconstructed images under different visual angles; 3) determining a back projection weighting coefficient: determining a reference value of the layered images according to the reconstructed images at different view angles in the step 2), calculating dissimilarity between the layered images at different projection view angles according to the reference value, and determining the back projection weighting coefficient according to the dissimilarity and adjustment parameters alpha and beta; and 4) performing back projection reconstruction: performing back projection reconstruction by using the back projection weighting coefficient obtained in the step 3), thereby solving the problems of incomplete projection data, image limited angle artifacts, layered image blurring and the like caused by the fact that the plate-shaped part is not suitable for full-angle scanning, reducing layered image aliasing, and improving the quality of the reconstructed image.

The invention discloses a CT image sparse reconstruction method of an iterative evolution model. The method comprises the following steps: acquiring sparse projection data with a fixed projection angle range or a projection angle interval reaching a set value; Generating four initial solutions, namely a pseudo-inverse solution, a random value solution, a total 0 value solution and a total 1 valuesolution; obtaiing Four groups of solution sets through random walk; Performing validity constraint on each solution in each solution set; Evaluating each solution in each group of random solution setaccording to a fitness evaluation function; transferring each solution according to a novel iteration evolution model; Updating each solution according to a convex optimization-based iterative algorithm; Selecting the current optimal solution update of each group according to the fitness evaluation; And if so, selecting an optimal solution as a reconstruction result according to the fitness evaluation. The method can improve the quality of the reconstructed image, and reduces the reconstruction artifacts.