Dr. Huifeng Tao Profile

Dr. Huifeng Tao

at Electronic Engineering Institute

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SPIE Journal Paper | 20 December 2016

Compressive imaging reconstruction using adaptive directional total variation based on structure orientation fields

Huifeng Tao, Xing Yang, Songfeng Yin, Yongshun Ling

JEI, Vol. 25, Issue 06, 063023, (December 2016) https://doi.org/10.1117/12.10.1117/1.JEI.25.6.063023

KEYWORDS: Compressive imaging, Reconstruction algorithms, Image quality, Visualization, Diffusion, Image analysis, Roads, Quality measurement, Signal processing, Optimization (mathematics)

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Compressive imaging presents the challenge of reconstructing an image accurately from its under-sampling measurements. The total variation model, used as the regularization for imaging reconstruction, ignores structure direction, which tends to result in over-smoothing of homogenous regions and staircase artifacts in textured regions. This paper proposes to solve this problem using a compressive reconstruction method with adaptive directional total variation (DTV) based on the structure orientation field (SOF). This approach builds the estimation model of the structure tensor upon the Huber norm as the penalty term, reducing the degradation influence and reaching a quick solution using the primal-dual algorithm. The method then uses the structure tensor to describe the SOF and to establish compressive reconstruction in which the SOF restricts the DTV regularization. Finally, by analyzing the structure of the objective function, this approach adopts the linearized alternating direction method of multipliers to address the challenges presented by the reconstruction model. The experimental results show that the proposed method requires fewer iterations and demonstrates better reconstruction performance than existing methods, as indicated by examining texture details for visual fidelity and quality as measured through other objective criteria.

Proceedings Article | 1 November 2016 Paper

Toeplitz block circulant matrix optimized with particle swarm optimization for compressive imaging

Huifeng Tao, Songfeng Yin, Cong Tang

Proceedings Volume 10157, 1015728 (2016) https://doi.org/10.1117/12.2246781

KEYWORDS: Particle swarm optimization, Compressive imaging

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Compressive imaging is an imaging way based on the compressive sensing theory, which could achieve to capture the high resolution image through a small set of measurements. As the core of the compressive imaging, the design of the measurement matrix is sufficient to ensure that the image can be recovered from the measurements. Due to the fast computing capacity and the characteristic of easy hardware implementation, The Toeplitz block circulant matrix is proposed to realize the encoded samples. The measurement matrix is usually optimized for improving the image reconstruction quality. However, the existing optimization methods can destroy the matrix structure easily when applied to the Toeplitz block circulant matrix optimization process, and the deterministic iterative processes of them are inflexible, because of requiring the task optimized to need to satisfy some certain mathematical property. To overcome this problem, a novel method of optimizing the Toeplitz block circulant matrix based on the particle swarm optimization intelligent algorithm is proposed in this paper. The objective function is established by the way of approaching the target matrix that is the Gram matrix truncated by the Welch threshold. The optimized object is the vector composed by the free entries instead of the Gram matrix. The experimental results indicate that the Toeplitz block circulant measurement matrix can be optimized while preserving the matrix structure by our method, and result in the reconstruction quality improvement.

Proceedings Article | 25 October 2016 Paper

Simulations of infrared atmospheric transmittance based on measured data

Fu-yin Song, Yuan Lu, Ya Qiao, Hui-feng Tao, Cong Tang, Yong-shun Ling

Proceedings Volume 10157, 101571E (2016) https://doi.org/10.1117/12.2246466

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There are two regular methods to calculate infrared atmospheric transmittance, including empirical formula and professional software. However, it has large deviations to use empirical formula. It is complicated to use professional software and difficult to apply in other infrared simulative system. Therefore, based on measured atmospheric data in some area for many years, article used the method of molecular single absorption to calculate absorption coefficients of water vapor and carbon dioxide in different temperature. Temperatures, pressures, and consequent scattering coefficients which distributed in different high were fitted with analysis formula according to different months. Then, it built simulative calculation model of atmospheric transmittance of infrared radiation. The simulative results are very close to accuracy results calculated by user-defined model of MODTRAN. The method is easy and convenient to use and has certain referent value in the project application.

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