access icon free Multi-frame blind deconvolution of atmospheric turbulence degraded images with mixed noise models

A mixed noise model is proposed and the multi-frame blind deconvolution is used to restore the images of space objects under the Bayesian inference framework. To minimise the cost function, an algorithm based on iterative recursion was proposed. In addition, three limited bandwidth constraints of the point spread functions were imposed into the solution process to avoid converging to local minima. Experimental results show that the proposed algorithm can effectively restore the turbulence degraded images and alleviate the distortion caused by the noise.

Inspec keywords: iterative methods; deconvolution; Bayes methods; atmospheric turbulence; image restoration; inference mechanisms

Other keywords: mixed noise model; multiframe blind deconvolution; point spread function; space object image restoration; Bayesian inference framework; cost function minimisation; three limited bandwidth constraint; iterative recursion; atmospheric turbulence

Subjects: Interpolation and function approximation (numerical analysis); Other topics in statistics; Knowledge engineering techniques; Optical, image and video signal processing; Other topics in statistics; Interpolation and function approximation (numerical analysis); Computer vision and image processing techniques

References

    1. 1)
    2. 2)
    3. 3)
http://iet.metastore.ingenta.com/content/journals/10.1049/el.2017.4277
Loading

Related content

content/journals/10.1049/el.2017.4277
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading