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Existing methods for spectral reconstruction usually learn a discrete mappingfrom RGB images to a number of spectral bands. However, this modeling strategyignores the continuous nature of spectral signature. In this paper, we proposeNeural Spectral Reconstruction (NeSR) to lift this limitation, by introducing anovel continuous spectral representation. To this end, we embrace the conceptof implicit function and implement a parameterized embodiment with a neuralnetwork. Specifically, we first adopt a backbone network to extract spatialfeatures of RGB inputs. Based on it, we devise Spectral Profile Interpolation(SPI) module and Neural Attention Mapping (NAM) module to enrich deep features,where the spatial-spectral correlation is involved for a better representation.Then, we view the number of sampled spectral bands as the coordinate ofcontinuous implicit function, so as to learn the projection from deep featuresto spectral intensities. Extensive experiments demonstrate the distinctadvantage of NeSR in reconstruction accuracy over baseline methods. Moreover,NeSR extends the flexibility of spectral reconstruction by enabling anarbitrary number of spectral bands as the target output.

Continuous Spectral Reconstruction from RGB Images via Implicit Neural Representation