A General-purpose Pipeline for Realistic Synthetic Multispectral Image Dataset Generation

A pipeline for the generation of synthetic dataset of spectral scenes, with corresponding sensor readings, is here proposed. The pipeline is composed of two main parts: Part 1: Image pixel reflectance assignment. Individual pixels from an input sRGB image dataset are replaced with appropriate reflectance spectra from a given non-image reflectance dataset. The resulting dataset of reflectance images is considered the starting point for simulated sensor acquisition. Part 2: Simulated sensor acquisition. Each spectral reflectance image in the dataset is illuminated with an illuminant spectra to produce a radiance image. The resulting dataset of radiance images is then synthetically read from the simulated sensors (camera and ambient multispectral sensor) of the Huawei P50 phone, using the corresponding sensors transmittance information. The capability of generating any large-scale, diverse, and annotated synthetic spectral datasets can facilitate the development of data-driven imaging algorithms, and foster reproducible research.