Learning Extended Depth of Field Hyperspectral Imaging
Erdem Sahin, Ugur Akpinar, Ayoung Kim, Atanas Gotchev
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SPS
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We propose a learning-based method for snapshot hyperspectral (HS) imaging of deep 3D scenes. The method combines computational HS imaging and extended depth of field (EDoF) imaging capabilities in a single framework, resulting in novel EDoF-HS camera designs. The camera system incorporates a diffractive optical element at the aperture position, a color filter array in front of the sensor and a residual dense network at the post-processing stage. These optical and neural components are jointly optimized through end-to-end learning procedure. We demonstrate high quality HS image reconstructions for scenes as deep as 4 diopters.