Titlebook: Scale Space and Variational Methods in Computer Vision; 7th International Co Jan Lellmann,Martin Burger,Jan Modersitzki Conference proceedi

自戀

978-3-030-22367-0Springer Nature Switzerland AG 2019

disciplined

WAX

Flustered

https://doi.org/10.1007/978-3-030-22368-7artificial intelligence; computer vision; estimation; image coding; image compression; image processing; i

重畫只能放棄

An Iteration Method for X-Ray CT Reconstruction from Variable-Truncation Projection Datae regularization parameter can be determined easily using the artifact severity estimation on the identified background points. Numerical experiments on simulated data with different noise levels are conducted to verify the effectiveness of the proposed method.

暴行

獸皮

Deep Eikonal Solversl solutions of the Eikonal equation computed on different surfaces. The proposed approach leverages the approximation power of neural networks to enhance the performance of numerical algorithms, thereby, connecting the somewhat disparate themes of numerical geometry and learning.

油氈

Towards PDE-Based Video Compression with Optimal Masks and Optic Flowease in the computational complexity. As a proof-of-concept, we evaluate the proposed approach for multiple sequences with different characteristics. We show that the method preserves a reasonable quality in the reconstruction, and is very robust against errors in the flow field.

elucidate

Iterative Sampled Methods for Massive and Separable Nonlinear Inverse Problemsonov method within a nonlinear optimization framework. The proposed method is computationally efficient in that it only uses available data at any iteration to update both sets of parameters. Numerical experiments applied to massive super-resolution image reconstruction problems show the power of these methods.

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