41 results about "Bregman iteration" patented technology

A system and method for projection number and radiation dose reduction in tomographic imaging that creates a three dimensional cross sectional image of an object by the reconstruction of its projections. Images of a superior quality can be obtained with a fewer number projections than seen with conventional methods by reconstruction of projections that have been pre-processed and preferably placed in the Fourier domain with a Fractional Fourier Transform (FrFT) or forward Pseudo-polar Fast Fourier transform (PPFFT). The projections are iteratively refined through formulation of a constrained optimization problem with constraints in object space and Fourier space preferably solved with a gradient descent algorithm incorporating a Bregman iterative regularization or a continuative regularization. By using an exact Fourier-based iterative algorithm as well as physical and mathematical constraints, convergence to a lowest-possible noise state that is also strictly consistent with the measured data can be obtained.

The invention discloses a polyethylene glycol terephthalate (PET) reconstruction method based on a sparsification and Poisson model. The PET reconstruction method includes: firstly, acquiring projection data, determining image size range and pixel range, and calculating a system probability matrix; obtaining an initial image through a filtered back projection (FBP) traditional algorithm, and using an obtained log-likelihood function as a reconstruction recovery item; using a wavelet transformation and discrete cosine transformation (DCT) mixed base and weighting as sparse regularization constraint, decomposing an objective function by using a split Bregman method to obtain two sub-problems, using a first sub-problem as a sparse regularization problem under a Gaussian model, and using linear Bregman interation for solving; using a second sub-problem as a Poisson denoising problem, and using a close operator method for solving; operating the last one iterative formula, completing an integrated iteration, obtaining a reconstructed image, and serving as an initial value of the next iteration.

The invention provides a Split Bregman weight iteration image blind restoration method based on a non-convex higher-order total variation model, and belongs to the technical field of image processing. The method is characterized in that firstly, a non-convex higher-order total variation regularization blind restoration cost function is obtained by introducing image border sparse prior information meeting a hyper-Laplacian model and by combining a high-order filter bank capable of generating piecewise linear solutions; secondly, a weight iteration strategy is provided, a minimization problem of the non-convex higher-order total variation regularization blind restoration cost function is converted into a minimization problem of an approximate convexity cost function with the updated weight; thirdly, the minimization problem of the approximate convexity cost function with the updated weight is converted into a new constraint solving problem through an operator split technology, and the constraint solving problem is converted into a split cost function through the method of adding a penalty term; fourthly, the split cost function is solved through a Split Bregman iteration solving frame. According to the Split Bregman weight iteration image blind restoration method based on the non-convex higher-order total variation model, an image can be restored effectively and rapidly, the shortage that a staircase effect is generated in a traditional total variation regularization blind restoration method is overcome, and meanwhile a better restoration effect on manually degraded images and actually degraded images is achieved.

The invention discloses a fast and effective dynamic MRI method based on a CS compressive sensing technology due to the fact that an existing imaging method is low in speed and high in hardware cost. The method comprises the steps that under a set accelerated factor, irrelevant sampling trajectories in k-t space are generated, and then data are collected according to the trajectories; objective functions are established by the utilization of the composite information of a dynamic MRI image sparse and low rank structure; finally, the composite regularization objective functions are solved through a kt-SLR method with Bregman iteration added, optimal solutions can be fast converged, and therefore original dynamic MRI image sequences are rebuilt. According to the method, the limit of the classic Nyquist sampling theorem is broken through, a small number of data points are sampled randomly, joint sparse information is combined, and the dynamic MRI image sequences are rebuilt through a fast nonlinear optimization algorithm of accelerating convergence. Therefore, MRI time is substantially shortened.

The invention discloses a spatial-spectral weighted TV-based hyperspectral-image restoration method of non-convex low-rank relaxation. Firstly, gradient information of local spatial neighborhoods is utilized to establish weighted TV of spatial-spectral combination; then a gamma norm of a matrix is introduced to be used as non-convex relaxation of a matrix rank under a framework of low-rank restoration of a hyperspectral image, and spatial-spectral weighted TV is combined to establish a hyperspectral-image non-convex-low-rank-restoration model of spatial-spectral weighted TV; an ADMM (Alternating Direction Method of Multipliers) is utilized to decompose the model into multiple sub-problems, and a non-convex soft-threshold operator, split Bregman iteration, a soft-threshold shrinkage operator and the like are respectively adopted to solve the sub-problems after conversion; and a hyperspectral image after restoration is obtained. The method fully mines spectral and spatial information ofthe hyperspectral image, has very-good spatial structure retention performance and spectral fidelity, has good unbiasedness and robustness at the same time, and can quickly and effectively remove mixed noises to obtain the hyperspectral image with a good visual effect.

The invention belongs to the field of seismic signal processing in seismic exploration technologies, and specifically discloses a primary wave and multiple wave separation method based on an alternative splitting Bregman iterative algorithm. With regard to a multiple wave self-adaptive subtraction method based on a 3D matched filter, the primary wave and multiple wave separation method utilizes the alternative splitting Bregman iterative algorithm to solve an optimization problem of applying sparsity constraint on primary waves, achieves the estimation of the 3D matched filter, and utilizes the estimated 3D matched filter to separate the primary waves and the multiple waves in a 3D data window in a self-adaptive manner. Compared with the traditional iterative reweighted least squares algorithm, the alternative splitting Bregman iterative algorithm adopted by the primary wave and multiple wave separation method only needs to calculate matrix-matrix multiplication and matrix inversion once when estimating the 3D matched filter at each 3D data window, can effectively reduce the calculation complexity of solving the optimization problem, and improves the calculating efficiency of primary wave and multiple wave self-adaptive separation.

The invention discloses a spectrum recover method based on the Laplacian-Markov field. The a spectrum recover method based on the Laplacian-Markov field comprises the following steps: conducting normalization processing on discretized spectrum to obtain normalized spectrum intensity; calculating a first derivative of the normalized spectrum intensity; calculating a neighborhood standard deviation of each normalized sectrum fi, finding out a maximum standard deviation and a minimum standard deviation, constructing a weighting matrix according to maximum standard deviation and the minimum standard deviation; and using the Laplacian-Markov field, and solving a raman spectrum deconvolution and split Bregman iteration method. The spectrum recover method based on the Laplacian-Markov field suppresses noise in places gentle in intensity change, adopts weak reatrain to store detail of the spectrum in places drastic in intensity change, achieves balance of recover resolution ratio and noise compress capability, introduces the split Bregman iteration method to overcome indifferentiable problem of conventional gradient descenting method, and provides technical support for application of the raman spectrum in quality detection, material analysis and the like.

The invention belongs to the field of digital image processing, and provides a fast robust image moving deblurring method based on splitting Bregman iteration. According to the method, a nonconvex nonsmooth energy functional for moving fuzzy kernel estimation is constructed through directly utilizing the image gradient and the L0 normal number of a moving fuzzy kernel and combining respective L2 normal numbers; a splitting Bregman iteration computational scheme of the moving fuzzy kernel is designed by coupling operator splitting and augmented Lagrangian methods; and the fast deblurring of images is realized by adopting an image non-blind deblurring method based on total variation priors. The fast robust image moving deblurring method has the advantages that through the introduction of support continuity priors, the estimation precision of the moving fuzzy kernel is improved; and a novel fast solution based on the splitting Bregman iteration is designed, and the estimation efficiency of the moving fuzzy kernel is greatly improved.

The invention discloses a foresight scanning radar super-resolution imaging method based on splitting Bregman. The foresight scanning radar super-resolution imaging method is applied to the technicalfield of radar imaging. By introducing the sparse goal constraint under a Bayes framework, the foresight scanning radar angular super-resolution problem is converted into the L1 regularization problem; then according to the Bregman iteration method, a Bregman iteration strategy of the L1 regularization problem is derived; then according to the splitting Bregman thought, variable separation is conducted, and the target variable is solved; finally, the rest variables are updated, algorithm terminal judgment is conducted, and through crossed iteration, the azimuth resolution is increased.

The invention discloses a real-time compression and reconstruction method of a Video-SAR (Synthetic Aperture Radar) image. A Robust-PCA (Principal Component Analysis) is used for effectively inhibiting coherent speckle noise in the Video-SAR image, and detail texture characteristics are favorably retained. Through the training and learning of an optimal Wavelet packet basis dictionary, good sparse representation on the Video-SAR image is realized so as to reduce a measurement amount, improve reconstruction precision and store the parse matrixes of different types of scenes, and the parse matrixes are directly called during dilution representation to reduce an operation amount. The frame difference of the key frame and the non-key frame of the Video-SAR image is subjected to low-complexity compression coding at different sampling rates, and distributed compression perception reconstruction can be adopted to meet real-time transmission requirements. A Canny operator is used for carrying out edge detection to realize a purpose that the non-key frame is subjected to Bregman iteration fine decoding, and the high-resolution SAR image with rich details can be reconstructed.

The invention provides a separation Bregman iterative optimization based image restoration method and device. The method comprises: establishing an objective function of an image restoration model based on block likelihood logarithm expectation, and converting the objective function into a finite form by introducing an intermediate auxiliary variable; calling the separation Bregman iterative optimization algorithm to divide the finite-form objective function into iterative calculation formulas about zi and X, and calculating the zi and X when the maximum number of iterations is obtained; and outputting the X as a restored image when the maximum number of iterations is reached. The invention can perform division and iterative calculation on the objective function by introducing the intermediate auxiliary variable and using the separation Bregman iterative optimization algorithm, and can improve the convergence speed of image restoration and effectively improve the image restoration effect.

The invention discloses a joint regularization based video super-resolution reconstruction method, which comprises the steps of building a low-resolution observation model; building a universal video reconstruction cost function through a regularization based least mean square method; constructing a compensation based TV (CTV) regularization item, and assigning an adaptive weighting coefficient to a regional space so as to reduce adverse effects brought about by a registration error; constructing a multi-frame nonlocal low-rank (MNLR) regularization item; building a joint regularization based video super-resolution reconstruction cost function; solving the cost function by using a Split-Bregman iteration method so as to reconstruct high-resolution video. Video frames reconstructed according to the video super-resolution reconstruction method have abundant edge information, and hardly have any sawtooth effects. It is observed from the video reconstruction frames that the method disclosed by the invention is also excellent in ability of noise suppression, and the method has a very high reference value in an objective evaluation parameter. Therefore, disclosed by the invention is an effective video super-resolution reconstruction method.

Provided is a non-local TV model image denoising method based on singular value weight functions. The method includes the steps of (1) inputting a noise image; (2) setting the relative parameters of the algorithm, including the non-local search window size N1*N1, neighborhood window size N2*N2, pixel similarity weight function parameters h, j, Gaussian kernel standard deviation sigma, split Bregman iterative auxiliary variable initial value b0, fidelity parameter gamma, and smoothing parameter Theta; (3) obtaining the largest singular value of an image block through a singular value decomposition method; (4) constructing a new pixel similarity weight function based on the largest singular value; (5) establishing a non-local TV model by using the weight function constructed in step (4); (6) solving the non-local TV model established in step (5) by using a split Bregman algorithm; (7) obtaining the denoised image by the split Bregman algorithm value iterative operation; (8) if the iteration satisfies the stop condition, outputting the iterative optimal result image and going to step (9), if the iteration doesn't satisfy the stop condition, returning to step (7) to continue the iteration; and (9) taking the iterative optimal result image in step (8) as the final denoising result image.

The invention discloses a noise image deblurring method based on mixed data fitting and weighted total variation. The implementation steps include 1. inputting a noise blurred image f of M rows and N columns; 2. establishing a model and initializing model parameters; 3. combining a convex subtraction algorithm and a separable Bregman iteration method to solve a target clear image u; and 4. judging whether iteration reaches a stop standard tol, and if the iteration does not reach the stop standard, continuing to circulate iteration in the Step 3, and otherwise outputting a restored image. The model in the method adopts a mixed data fitting term, thereby ensuring that image details are well restored; a weighted total variation regularization prior model is utilized to perform approximate simulation on gradient distribution of a natural image, so that a restoration result is relatively acute; and the separable Bregman iteration method is utilized, and thus a high-quality clear image can be solved rapidly. The noise image deblurring method provided by the invention has the advantage of good reconstructed image edge texture structure maintenance, and can be used for digital image processing in the fields of medical science, astronomy, video multimedia and the like.

The invention requests to protect an image compressed sensing reconstruction method based on a non-local self-similarity model, and belongs to the field of signal and image processing. Specifically, in order to improve the quality of traditional image compressed sensing reconstruction, prior information of the image is utilized; a non-local self-similarity model of the image is constructed; a weight matrix of an image block is calculated; constructing an adaptive non-local regularization item of the image by utilizing the non-local self-similarity priori information of the image block; a mathematical model of image compressed sensing reconstruction is provided, and an efficient Spant Bregman Iteration (SBI) algorithm is used for alternate iteration updating, so that the reconstruction performance of image compressed sensing is improved. Meanwhile, in the learning process of the dictionary, a training sample is extracted through a current approximate estimation image d, and K-is utilized; and the SVD algorithm is alternately updated to obtain a self-adaptive learning dictionary. The adaptive image compressed sensing reconstruction method of the non-local self-similarity model provided by the invention has universal significance in practice. The image compressed sensing reconstruction quality is effectively improved, the block effect of the image is reduced, the texture and details of the image are kept not lost, and the texture and details of the image are better depicted.

Apparatus and method for obtaining a plurality of spectral images of a source object in a snapshot using comprising two-dimensional compressed sensing data cube reconstruction (2D CS- SCR) applied to a dispersed-diffused snapshot image. In some embodiments, the snapshot image is obtained through a RIP diffuser. In some embodiments, a randomizer is used to further randomized the dispersed-diffused snapshot image. The 2D CS-SCR includes applying a 2D framelet transform separately to arrays representing different wavebands of spectral cube data derived from the snapshot image. The application of the 2D framelet transform separately to the arrays representing the different wavebands includes application of direct and inverse 2D framelet transforms to the arrays. In some embodiments, the direct and inverse framelet transforms are included in a split Bregman iteration.

The invention provides a fluorescent microscopic image three-dimensional reconstruction method and system based on compound regularization technology. The method comprises the steps of acquiring a fluorescent microscopic image, and establishing a degeneration model for the fluorescent microscopic image; carrying out compound regularization constraints of generalized total variation and shearlet transform for the degeneration model, to obtain an object function of fluorescent microscopic image reconstruction; obtaining an unconstrained object function equivalent to the object function according to the theory of separation of variable; and according to the unconstrained object function, calculating a reconstructed image of the fluorescent microscopic image based on a split Bregman iterative algorithm. The method of the invention can obtain an optimal reconstructed original effect by reasonable selection of parameters, and is high in effectiveness and feasibility.

The invention discloses a multi-band multi-view ISAR fusion imaging method, and relates to the technical field of image processing methods. According to the method, firstly, a vectorization sparse representation model of multi-band multi-view ISAR fusion imaging is established, and in order to reduce the calculation complexity during solving and improve the imaging efficiency, on the basis of a Bregman iteration algorithm, the iteration convergence speed of the algorithm is further improved in combination with weighted residual error back substitution and basis matrix condition number optimization; a simulation experiment verifies the effectiveness and superiority of the algorithm in realizing one-dimensional sparse signal reconstruction and multi-band multi-view ISAR fusion imaging.