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Multi-frame blind deconvolution of atmospheric turbulence degraded images with mixed noise models
| Content Provider | Semantic Scholar |
|---|---|
| Author | Yang, Afeng Jiang, Xue Li, David Day-Uei |
| Copyright Year | 2017 |
| Abstract | This paper proposes a mixed noise model and uses the multi-frame blind deconvolution to restore the images of space objects under the Bayesian inference framework. To minimize 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. |
| Starting Page | 206 |
| Ending Page | 208 |
| Page Count | 3 |
| File Format | PDF HTM / HTML |
| DOI | 10.1049/el.2017.4277 |
| Volume Number | 54 |
| Alternate Webpage(s) | https://pure.strath.ac.uk/ws/portalfiles/portal/71024293/Yang_atal_EL_2017_Multi_frame_blind_deconvolution_of_atmospheric_turbulence.pdf |
| Alternate Webpage(s) | https://strathprints.strath.ac.uk/62591/1/Yang_atal_EL_2017_Multi_frame_blind_deconvolution_of_atmospheric_turbulence.pdf |
| Alternate Webpage(s) | https://core.ac.uk/download/pdf/141470714.pdf |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
