Learned Spectral Super-Resolution

Understand

We describe a novel method for blind, single-image spectral super-resolution.

• While conventional super-resolution aims to increase the spatial resolution of an input image, our goal is to spectrally enhance the input, i.e., generate an image with the same spatial resolution, but a greatly increased number of narrow (hyper-spectral) wave-length bands.
• Just like the spatial statistics of natural images has rich structure, which one can exploit as prior to predict high-frequency content from a low resolution image, the same is also true in the spectral domain: the materials and lighting conditions of the observed world induce structure in the spectrum of wavelengths observed at a given pixel.
• Surprisingly, very little work exists that attempts to use this diagnosis and achieve blind spectral super-resolution from single images.