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 present invention relates to a device and a method for obtaining multiple spectral images of a source object in a snapshot, including the use of two-dimensional compressed sensing data cube reconstruction (2D CS-SCR) applied to the divergent-diffuse snapshot image. In some embodiments, the snapshot image is obtained through a RIP diffuser. In some embodiments, a randomizer is used to further randomize the divergence-diffuse snapshot images. 2D CS‑SCR consists of applying a 2D framelet transform individually to each array representing the different bands of spectral cube data derived from snapshot images. Applying the 2D small frame transformation individually to each array representing the different bands includes applying a direct and an inverse 2D small frame transformation to each array. In some embodiments, the direct and inverse small frame transformations are included in split Bregman iterations.

The invention provides a high spectral image fusion method based on a variational method, which is a high spectral image and high-resolution visible light image fusion method based on a variational method. The fusion method comprises the following three steps: (1) reading a high spectral image data by a computer in an MATLAB7.1 environment; (2) creating a functional expression based on a variational method, and selecting appropriate weighting coefficients; and (3) solving an extremal function of the functional expression by using a splitting Bregman iterative algorithm to obtain a fusion result. The invention overcomes the disadvantages that a high spectral image fusion is not easily to be explanted directly and the retentivity of spectrum is not good in the prior art. The invention achieves a good fusion effect and has a wide application prospect in the technical field of high spectral image fusion.

The invention relates to a total-variation (TV) regularized image blind restoration method based on Split Bregman iteration, belonging to the field of image processing technology. The method comprises the following key steps: constructing a TV regularized blind restoration cost function based on prior information and regularization features of images; converting a minimization problem into a new constraint solving problem by using an operator splitting technique; converting the constraint solving problem into a split cost function by using a method added with penalty terms; and proposing an extended Split Bregman iteration solving frame to solve the split cost function. Experiments show that the method provided by the invention can effectively and rapidly restore the image, and can restore a plurality of blur types. Additionally, the method can overcome the disadvantage of poor restoration effect for complicated blur type or complicated image of the conventional TV regularized blind restoration method, and the method has good noise robustness.

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 noise image deblurring method based on mixed data fitting and weighted total variation. The implementation steps are as follows: step 1: input a noise blurred image f with M rows and N columns; step 2: establish a model and Initialize the model parameters; Step 3: Combine the convex subtraction algorithm and the separable Bregman iteration method to solve the target clear image u; Step 4: Determine whether the iteration reaches the stop standard tol, if not, continue the iteration in step 3, Otherwise output the restored image. The model of the present invention adopts mixed data fitting items to ensure better restoration of image details; uses the regularized prior model of weighted total variation to approximate the gradient distribution of natural images, making the restoration results more accurate; uses separable Bregman The iterative method can quickly solve high-quality clear images. The invention has the advantage of keeping the edge texture structure of the reconstructed image well, and can be used for digital image processing in the fields of medicine, astronomy, video multimedia and the like.

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.