Computational imaging from non-uniform degradation of staggered TDI thermal infrared imager
For the Time Delay Integration (TDI) staggered line-scanning thermal infrared imager, a Computational Imaging (CI) approach is developed to achieve higher spatial resolution images. After a thorough analysis of the causes of non-uniform image displacement and degradation for multi-channel staggered...
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| Published in | Optics express Vol. 23; no. 19; p. 24572 |
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| Main Authors | , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
United States
21.09.2015
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| Online Access | Get full text |
| ISSN | 1094-4087 1094-4087 |
| DOI | 10.1364/OE.23.024572 |
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| Abstract | For the Time Delay Integration (TDI) staggered line-scanning thermal infrared imager, a Computational Imaging (CI) approach is developed to achieve higher spatial resolution images. After a thorough analysis of the causes of non-uniform image displacement and degradation for multi-channel staggered TDI arrays, the study aims to approach one-dimensional (1D) sub-pixel displacement estimation and superposition of images from time-division multiplexing scanning lines. Under the assumption that a thermal image is 2D piecewise C(2) smooth, a sparse-and-smooth deconvolution algorithm with L1-norm regularization terms combining the first and second order derivative operators is proposed to restore high frequency components and to suppress aliasing simultaneously. It is theoretically and experimentally demonstrated, with simulation and airborne thermal infrared images, that this is a state-of-the-art practical CI method to reconstruct clear images with higher frequency components from raw thermal images that are subject to instantaneous distortion and blurring. |
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| AbstractList | For the Time Delay Integration (TDI) staggered line-scanning thermal infrared imager, a Computational Imaging (CI) approach is developed to achieve higher spatial resolution images. After a thorough analysis of the causes of non-uniform image displacement and degradation for multi-channel staggered TDI arrays, the study aims to approach one-dimensional (1D) sub-pixel displacement estimation and superposition of images from time-division multiplexing scanning lines. Under the assumption that a thermal image is 2D piecewise C(2) smooth, a sparse-and-smooth deconvolution algorithm with L1-norm regularization terms combining the first and second order derivative operators is proposed to restore high frequency components and to suppress aliasing simultaneously. It is theoretically and experimentally demonstrated, with simulation and airborne thermal infrared images, that this is a state-of-the-art practical CI method to reconstruct clear images with higher frequency components from raw thermal images that are subject to instantaneous distortion and blurring. For the Time Delay Integration (TDI) staggered line-scanning thermal infrared imager, a Computational Imaging (CI) approach is developed to achieve higher spatial resolution images. After a thorough analysis of the causes of non-uniform image displacement and degradation for multi-channel staggered TDI arrays, the study aims to approach one-dimensional (1D) sub-pixel displacement estimation and superposition of images from time-division multiplexing scanning lines. Under the assumption that a thermal image is 2D piecewise C(2) smooth, a sparse-and-smooth deconvolution algorithm with L1-norm regularization terms combining the first and second order derivative operators is proposed to restore high frequency components and to suppress aliasing simultaneously. It is theoretically and experimentally demonstrated, with simulation and airborne thermal infrared images, that this is a state-of-the-art practical CI method to reconstruct clear images with higher frequency components from raw thermal images that are subject to instantaneous distortion and blurring.For the Time Delay Integration (TDI) staggered line-scanning thermal infrared imager, a Computational Imaging (CI) approach is developed to achieve higher spatial resolution images. After a thorough analysis of the causes of non-uniform image displacement and degradation for multi-channel staggered TDI arrays, the study aims to approach one-dimensional (1D) sub-pixel displacement estimation and superposition of images from time-division multiplexing scanning lines. Under the assumption that a thermal image is 2D piecewise C(2) smooth, a sparse-and-smooth deconvolution algorithm with L1-norm regularization terms combining the first and second order derivative operators is proposed to restore high frequency components and to suppress aliasing simultaneously. It is theoretically and experimentally demonstrated, with simulation and airborne thermal infrared images, that this is a state-of-the-art practical CI method to reconstruct clear images with higher frequency components from raw thermal images that are subject to instantaneous distortion and blurring. |
| Author | Zhang, Zijian Chen, Wangli Sun, Tao Qin, Qianqing Shi, Yan Liu, Jian Guo |
| Author_xml | – sequence: 1 givenname: Tao surname: Sun fullname: Sun, Tao – sequence: 2 givenname: Jian Guo surname: Liu fullname: Liu, Jian Guo – sequence: 3 givenname: Yan surname: Shi fullname: Shi, Yan – sequence: 4 givenname: Wangli surname: Chen fullname: Chen, Wangli – sequence: 5 givenname: Qianqing surname: Qin fullname: Qin, Qianqing – sequence: 6 givenname: Zijian surname: Zhang fullname: Zhang, Zijian |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/26406660$$D View this record in MEDLINE/PubMed |
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