48 results about "Iterative strategy" patented technology

The invention discloses a Raman spectral image unmixing method based on non-negative matrix proximity. The method aims at the absence of impure pixels in the Raman spectral image, and unknown ANC, ASC and endmembers in the Raman spectral unmixing. When the Raman spectral image satisfies the LMM model, by adding a variety of regularization factors such as sparseness to the non-negative matrix, the components of the mixed pixels and their corresponding abundances can be effectively obtained. Compared with other existing methods, the present invention has the following advantages: (1) the number of endmembers (constituent substances) is not required as prior knowledge; (2) an iterative strategy is adopted, and the obtained result has nothing to do with the number of endmembers; (3) The graph relationship and 1 / 2 norm are added to improve the noise resistance and stability of the algorithm.

The invention discloses a UAV path planning method based on second-order cone optimization, including: establishing a two-dimensional UAV motion model and transforming it; Description form; Convexize and relax the non-convex constraints existing in the problem, so that it can satisfy the second-order cone optimization framework, and introduce the L1 penalty function in the no-fly zone constraint to ensure the solvability of the problem; based on the above-mentioned various The cost function is constrained to design, so that the transformed problem is equivalent to the original problem; the problem is discretized, and a successive iteration strategy is given to solve it under the framework of second-order cone optimization. The present invention adopts the path planning method based on second-order cone optimization, which not only ensures that the designed path is the global optimum, but also directly solves the state quantity and control input of the UAV, avoiding additional design of control laws, and, in When applied, it also has the advantages of high calculation efficiency and easy expansion of the number and types of constraints.

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.

Multiscale deep reinforcement machine learning for N-dimensional segmentation in medical imaging. Multiscale deep reinforcement learning produces multiscale deep reinforcement models for multidimensional (e.g., 3D) segmentation of objects (22). In this context, segmentation is formulated as learning an image-driven policy (38) for shape evolution (40) that converges to object boundaries. This segmentation is treated as a reinforcement learning problem, and scale-space theory is used to achieve robust and efficient multi-scale shape estimation. The learning challenge of end-to-end regression systems can be addressed by learning an iterative strategy to find segmentations.