Spectral-wise Implicit Neural Representation for Hyperspectral Image
  Reconstruction
Huan Chen Wangcai Zhao Tingfa Xu Shiyun Zhou Peifu Liu Jianan Li
Coded Aperture Snapshot Spectral Imaging (CASSI) reconstruction aims to
recover the 3D spatial-spectral signal from 2D measurement. Existing methods
for reconstructing Hyperspectral Image (HSI) typically involve learning
mappings from a 2D compressed image to a predetermined set of discrete spectral
bands. However, this approach overlooks the inherent continuity of the spectral
information. In this study, we propose an innovative method called
Spectral-wise Implicit Neural Representation (SINR) as a pioneering step toward
addressing this limitation. SINR introduces a continuous spectral amplification
process for HSI reconstruction, enabling spectral super-resolution with
customizable magnification factors. To achieve this, we leverage the concept of
implicit neural representation. Specifically, our approach introduces a
spectral-wise attention mechanism that treats individual channels as distinct
tokens, thereby capturing global spectral dependencies. Additionally, our
approach incorporates two components, namely a Fourier coordinate encoder and a
spectral scale factor module. The Fourier coordinate encoder enhances the
SINR's ability to emphasize high-frequency components, while the spectral scale
factor module guides the SINR to adapt to the variable number of spectral
channels. Notably, the SINR framework enhances the flexibility of CASSI
reconstruction by accommodating an unlimited number of spectral bands in the
desired output. Extensive experiments demonstrate that our SINR outperforms
baseline methods. By enabling continuous reconstruction within the CASSI
framework, we take the initial stride toward integrating implicit neural
representation into the field.