Adaptive foveated single-pixel imaging with dynamic supersampling
| 文献类型 | 期刊 |
| 作者 | David B. Phillips[1];Sun Ming-Jie[2];Jonathan M. Taylor[3];Matthew P. Edgar[4];Stephen M. Barnett[5];Graham M. Gibson[6];Miles J. Padgett[7] |
| 机构 | [1]School of Physics and Astronomy, University of Glasgow, Glasgow G12 8QQ, UK. |
| 通讯作者 | David B. Phillips;Sun Ming-Jie; |
| 来源信息 | 年:2017 卷:3 期:4 页码范围:e1601782 |
| 期刊信息 | Science Advances |
| 摘要 | In contrast to conventional multipixel cameras, single-pixel cameras capture images using a single detector that measures the correlations between the scene and a set of patterns. However, these systems typically exhibit low frame rates, because to fully sample a scene in this way requires at least the same number of correlation measurements as the number of pixels in the reconstructed image. To mitigate this, a range of compressive sensing techniques have been developed which use a priori knowledge to reconstruct images from an undersampled measurement set. Here, we take a different approach and adopt a strategy inspired by the foveated vision found in the animal kingdom{\textemdash}a framework that exploits the spatiotemporal redundancy of many dynamic scenes. In our system, a high-resolution foveal region tracks motion within the scene, yet unlike a simple zoom, every frame delivers new spatial information from across the entire field of view. This strategy rapidly records the detail of quickly changing features in the scene while simultaneously accumulating detail of more slowly evolving regions over several consecutive frames. This architecture provides video streams in which both the resolution and exposure time spatially vary and adapt dynamically in response to the evolution of the scene. The degree of local frame rate enhancement is scene-dependent, but here, we demonstrate a factor of 4, thereby helping to mitigate one of the main drawbacks of single-pixel imaging techniques. The methods described here complement existing compressive sensing approaches and may be applied to enhance computational imagers that rely on sequential correlation measurements. |
| 所属部门 | 仪器科学与光电工程学院 |
| 链接地址 | http://advances.sciencemag.org/content/3/4/e1601782 |
| DOI | 10.1126/sciadv.1601782 |
| 人气指数 | 505 |
| 浏览次数 | 504 |
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