SpectralCam: High-Resolution Low-Cost Spectral Imaging Using DSLR Cameras

Abstract

Multi-spectral imaging is pivotal in numerous industrial, scientific, and medical fields, yet existing high-resolution systems often rely either on bulky prototypes or costly handheld setups. This paper introduces a novel approach to spectral imaging using a cost-effective handheld camera: the Spectral Camera (SpectralCam). It leverages the advanced optics, sensors, and electronics of a standard Canon EOS R100 DSLR (digital single-lens reflex) camera along with a composite 12-color, color-coded aperture (CCA) fabricated with Fuji Velvia 50 film to enhance light polarization into the DSLR. Additionally, we train a denoising diffusion probabilistic model (DDPM) and devise a guided diffusion workflow to reconstruct images across 12 and 24 spectral bands ranging from 430 to 660 nanometers. Notably, our method eliminates the need for application or hardware-specific training by leveraging the capability of generative artificial intelligence (AI), thus allowing for flexible adaptation to various experimental setups. The proposed solution demonstrates the potential to address diverse challenges in multi-spectral imaging by achieving high-resolution spectral data capture, improved adaptability and deployability while significantly reducing costs and complexity.