Ms. Jiao Zhang Profile

Jiao Zhang

at Xi'an Research Institute of High Technology

SPIE Involvement:

Author

Publications (2)

Proceedings Article | 24 October 2017 Paper

Image restoration from sequences under atmospheric turbulence effects

Junshan Li, Jiao Zhang, Zhongshan Sui

Proceedings Volume 10462, 104625G (2017) https://doi.org/10.1117/12.2285835

KEYWORDS: Atmospheric turbulence, Image restoration, Image registration, Infrared imaging, Infrared radiation

Read Abstract +

Proceedings Article | 24 November 2014 Paper

Fruit fly optimization based least square support vector regression for blind image restoration

Jiao Zhang, Rui Wang, Junshan Li, Yawei Yang

Proceedings Volume 9301, 93011W (2014) https://doi.org/10.1117/12.2072397

KEYWORDS: Image restoration, Point spread functions, Image processing, Deconvolution, Lawrencium, Expectation maximization algorithms, Cameras, Image quality, Optimization (mathematics), Evolutionary algorithms

Read Abstract +

The goal of image restoration is to reconstruct the original scene from a degraded observation. It is a critical and challenging task in image processing. Classical restorations require explicit knowledge of the point spread function and a description of the noise as priors. However, it is not practical for many real image processing. The recovery processing needs to be a blind image restoration scenario. Since blind deconvolution is an ill-posed problem, many blind restoration methods need to make additional assumptions to construct restrictions. Due to the differences of PSF and noise energy, blurring images can be quite different. It is difficult to achieve a good balance between proper assumption and high restoration quality in blind deconvolution. Recently, machine learning techniques have been applied to blind image restoration. The least square support vector regression (LSSVR) has been proven to offer strong potential in estimating and forecasting issues. Therefore, this paper proposes a LSSVR-based image restoration method. However, selecting the optimal parameters for support vector machine is essential to the training result. As a novel meta-heuristic algorithm, the fruit fly optimization algorithm (FOA) can be used to handle optimization problems, and has the advantages of fast convergence to the global optimal solution. In the proposed method, the training samples are created from a neighborhood in the degraded image to the central pixel in the original image. The mapping between the degraded image and the original image is learned by training LSSVR. The two parameters of LSSVR are optimized though FOA. The fitness function of FOA is calculated by the restoration error function. With the acquired mapping, the degraded image can be recovered. Experimental results show the proposed method can obtain satisfactory restoration effect. Compared with BP neural network regression, SVR method and Lucy-Richardson algorithm, it speeds up the restoration rate and performs better. Both objective and subjective restoration performances are studied in the comparison experiments.

View contact details

UPDATE YOUR PROFILE

Is this your profile? Update it now.

Sign into your SPIE.org account

Don’t have a profile and want one?

Create an account on SPIE.org

Keywords/Phrases

Search In:

Publication Years