.png){kind=icon}
返回
Deep learning enabled reflective coded aperture snapshot spectral imaging
.png){kind=icon}
PRE
AI
DOI：10.1364/OE.475129.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Coded aperture snapshot spectral imaging (CASSI) can acquire rich spatial and spectral information at ultra-high speed, which shows extensive application prospects. CASSI innovatively employed the idea of compressive sensing to capture the spatial-spectral data cube using a monochromatic detector and used reconstruction algorithms to recover the desired spatial-spectral information. Based on the optical design, CASSI currently has two different implementations: single-disperser (SD) CASSI and dual-disperser (DD) CASSI. However, SD-CASSI has poor spatial resolution naturally while DD-CASSI increases size and cost because of the extra prism. In this work, we propose a deep learning-enabled reflective coded aperture snapshot spectral imaging (R-CASSI) system, which uses a mask and a beam splitter to receive the reflected light by utilizing the reflection of the mask. The optical path design of R-CASSI makes the optical system compact, using only one prism as two dispersers. Furthermore, an encoder-decoder structure with 3D convolution kernels is built for the reconstruction, dubbed U-net-3D. The designed U-net-3D network achieves both spatial and spectral consistency, leading to state-of-the-art reconstruction results. The real data is released and can serve as a benchmark dataset to test new reconstruction algorithms.
Keyword:
ALGORITHMS
DESIGN
SYSTEM
期刊
IF:
3.3
论文数:
6.1W
被引数:
14.3W
机构
引用论文
分享收藏
Development of a digital-micromirror-device-based multishot snapshot spectral imaging system.png)
OPTICS LETTERS
OPTICS LETTERSIF3.3
PREAI
分享收藏
查看更多内容