2021 IEEE International Conference on Image Processing

19-22 September 2021 • Anchorage, Alaska, USA

Imaging Without Borders

2021 IEEE International Conference on Image Processing

19-22 September 2021 • Anchorage, Alaska, USA

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Paper Detail

Paper IDIMT-CIF-2.2
Paper Title Perception Inspired Deep Neural Networks for Spectral Snapshot Compressive Imaging
Authors Ziyi Meng, Beijing University of Posts and Telecommunications, China; Xin Yuan, Nokia Bell Lab, United States
SessionIMT-CIF-2: Computational Imaging 2
LocationArea I
Session Time:Wednesday, 22 September, 14:30 - 16:00
Presentation Time:Wednesday, 22 September, 14:30 - 16:00
Presentation Poster
Topic Computational Imaging Methods and Models: Coded Image Sensing
IEEE Xplore Open Preview  Click here to view in IEEE Xplore
Abstract We consider the inverse problem of coded aperture snapshot spectral imaging (CASSI), which captures the spatio-spectral data-cube using a snapshot 2D measurement and reconstructs the 3D hyperspectral images using algorithms. Recent advances of deep learning have boosted the image quality of the reconstructed hyperspectral images significantly, and this leads to an end-to-end real-time capture and reconstruction system. However, the network design for CASSI reconstruction is still at the incubation stage and usually an off-the-shelf network is employed and re-purposed. In this work, from a different perspective, inspired by the fact that most existing hyperspectral images are still in the visible bandwidth, we introduce the perceptual loss into the deep neural network for CASSI reconstruction. Extensive results on both simulation and real data demonstrate that with this small change, the reconstructed image quality can be improved dramatically using the same network.