Beijing University of Technology

Kebin Jia

Pengyu Liu

Jinchao Feng

Zhe Li

Huan Dou

Qiuwan Sun

Zhonghua Sun

Zuocheng Zhou

Huijun Wei

Yibai Wang

The present disclosure disclose a near-infrared spectroscopy tomography reconstruction method based on neural network which belongs to the field of medical image processing. In the Boltzmann radiation transmission equation, transmission process of light is regarded as absorption and scattering process of photons in medium, and interaction between light and tissue is determined by absorption coefficient, scattering coefficient and phase function of the response scattering distribution. In the transmission, only the particle property of light is taken into account, not the fluctuation of light. Therefore, polarization and interference phenomena related to the fluctuation of light are not considered, and only the energy transmission of light is tracked. The reconstruction method based on BP neural network is used to reconstruct the distribution of optical absorption coefficient, reconstruction results of absorption coefficient distribution can be obtained by calculation. This method can not only reconstruct the absorption coefficient distribution accurately, but also has high computational efficiency.

Near-infrared spectroscopy tomography reconstruction method based on neural network

A visual perception characteristics-combining hierarchical video coding method includes: setting priority of visual interest area and setting allocation scheme of video coding resource. Setting priority of visual interest area includes analyzing both time and space visual characteristic saliency of video content. Setting allocation scheme of video coding resource includes, according to the priority of visual interest area, macroblock coding resource of interest area is satisfied preferentially to realize hierarchical coding.

Visual perception characteristics-combining hierarchical video coding method

The present disclosure relates to a method of acquiring neighboring disparity vectors for multi-texture and multi-depth video. The method belongs to the area of 3D-HEVC video coding technology. The method includes changing the standard associated with a disparity vector that is first searched as a final disparity vector. By deleting location which is minimum searched in candidate space and time location of the coding unit next to current coding unit to divide candidate space and time location of the coding unit into groups, the method takes searched disparity vector that is combined based on the proportion of adoption rate as final disparity vector. The method improves coding quality and at the same time maintaining origin fast algorithm efficiency. The embodiments of the present disclosure improve coding quality at least 0.05% and at the same time maintain origin fast algorithm efficiency while the decoding time is decreased to 97.1%.

Method of acquiring neighboring disparity vectors for multi-texture and multi-depth video

Implementations of the present disclosure relate to methods for reconstruction for bioluminescence tomography based on a method of multitask Bayesian compressed sensing in the field of medical image processing. The method includes the following operations. Firstly the high order approximation model is used to model the law of light propagation in biological tissues, then the inner-correlation among multispectral measurements is researched based on multitask learning method and incorporated into a reconstruction algorithm of bioluminescence tomography as prior information to reduce ill-posedness of BLT reconstruction, and then on this basis, three-dimensional reconstruction of bioluminescent source is realized. Compared with other reconstruction algorithms for BLT, the correlation among multispectral measurements is incorporated into the disclosure and the ill-posedness of BLT reconstruction is reduced. The bioluminescent source can be reconstructed and located accurately using the proposed algorithm, and computational efficiency can be greatly improved.

Bioluminescence tomography reconstruction based on multitasking bayesian compressed sensing

The present disclosure relates to the technical field of video coding. Implementations herein provide methods for fast 3D video coding for high efficiency video coding HEVC. The methods speed up the view synthesis process during the rate distortion optimization for depth coding based on texture flatness. The implementations include extracting coding information from textures, analyzing luminance regularity among pixels from flat texture regions based on statistical method, judging the flat texture regions using the luminance regularity for depth maps and terminating the flat texture block's view synthesis process when processing rate distortion optimization. Compared to original pixel-by-pixel rendering methods, the implementations reduce coding time without causing significant performance loss.

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Beijing, China

Kebin Jia