In this paper, a new method of combination single layer wavelet transform and compressive sensing is proposed for image fusion. In which only measured the high-pass wavelet coefficients of the image but preserved the low-pass wavelet coefficient. Then, fuse the low-pass wavelet coefficients and the measurements of high-pass wavelet coefficient with different schemes. For the reconstruction, by using the minimization of total variation algorithm (TV), high-pass wavelet coefficients could be recovered by the fused measurements. Finally, the fused image could be reconstructed by the inverse wavelet transform. The experiments show the proposed method provides promising fusion performance with a low computational complexity.
Seeking high-performance computing methods to solve the problem of a large amount of calculation, low calculation efficiency, and small simulation scale on the traditional single central processing unit (CPU) platform is of great value to the simulation study of micro-structure. In this study, based on the three-dimensional multi-phase-field model of KKSO coupling phase-field and solute field, the open computing language (OpenCL) + graphics processing unit (GPU) heterogeneous parallel computing technology is used to simulate the eutectoid growth of Fe-C alloy and the end growth process of pearlite under pure diffusion. The effects of initial supercooling and different diffusion coefficients on the growth morphology of lamellar pearlite were investigated. The results show that ferrite and cementite are perpendicular to the front of the solid-solid interface and are coupled and coordinated to grow, and there is no leading phase under the initial supercooling degree of 20 K. With the continuous increase of the initial supercooling degree (19 K-22 K), the morphology changes of the eutectoid layer are as follows: cementite stops growing → slice amplitude increases → regular symmetric growth → oblique growth → layer merge. With the increase of the diffusion coefficient from 3×10 -13 m 2 •s -1 to 15×10 -13 m 2 •s -1 , the growth rate of the microstructure of the lamellar pearlite increases linearly, and there is no obvious change in the frontal appearance of the pearlite.
When appearance variation of object, partial occlusion or illumination change in object images occurs, most existing tracking approaches fail to track the target effectively. To deal with the problem, this paper proposed a robust visual tracking method based on appearance modeling and sparse representation. The proposed method exploits two-dimensional principal component analysis (2DPCA) with sparse representation theory for constructing appearance model. Then tracking is achieved by Bayesian inference framework, in which a particle filter is applied to evaluate the target state sequentially over time. In addition, to make the observation model more robust, the incremental learning algorithm is used to update the template set. Both qualitative and quantitative evaluations on four publicly available benchmark video sequences demonstrate that the proposed visual tracking algorithm performs better than several state-of-the-art algorithms.
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