论文标题
深度学习的宽带编码随机过滤器用于计算光谱仪器
Deep-Learned Broadband Encoding Stochastic Filters for Computational Spectroscopic Instruments
论文作者
论文摘要
具有编码随机过滤器的宽带的计算光谱仪器允许仅使用几个过滤器以高精度重建光谱。但是，最佳过滤器的传统设计举止通常是启发式的，并且可能无法充分探索最佳过滤器的编码潜力。参数限制的光谱编码器和解码器（PCSED） - 为基于神经网络的框架设计了用于光谱仪器中最佳过滤器的框架。通过全面合并目标光谱响应定义和光学设计程序，PCSED链接了可用制造技术所限制的数学最佳和实际限制。从中受益，最佳过滤器的光谱摄像头提出了更高的重建精度，其增强功能高达30倍，并且对制造错误的耐受性更好。 PCSED的普遍性在设计跨表面和干扰 - 薄膜的最佳过滤器方面得到了验证。
Computational spectroscopic instruments with Broadband Encoding Stochastic (BEST) filters allow the reconstruction of the spectrum at high precision with only a few filters. However, conventional design manners of BEST filters are often heuristic and may fail to fully explore the encoding potential of BEST filters. The Parameter Constrained Spectral Encoder and Decoder (PCSED) - a neural network-based framework is presented for the design of BEST filters in spectroscopic instruments. By incorporating the target spectral response definition and the optical design procedures comprehensively, PCSED links the mathematical optimum and practical limits confined by available fabrication techniques. Benefiting from this, the BEST-filter-based spectral camera present a higher reconstruction accuracy with up to 30 times' enhancement and a better tolerance on fabrication errors. The generalizability of PCSED is validated in designing metasurface- and interference-thin-film-based BEST filters.
