CVApr 15, 2025

Seeing like a Cephalopod: Colour Vision with a Monochrome Event Camera

arXiv:2504.10984v22 citationsh-index: 72025 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
Originality Incremental advance
AI Analysis

This work addresses the challenge of spectral imaging for applications requiring lightweight or bio-inspired sensors, though it is incremental as it adapts known biological principles to a new hardware context.

The researchers tackled the problem of achieving color vision with a monochrome event camera by mimicking cephalopod ocular mechanisms, resulting in a system that enables wavelength-dependent focusing across the visible and near-infrared spectrum without using color filters or demosaicing. They validated the approach in simulations and real setups, demonstrating robust spectral sensing capabilities.

Cephalopods exhibit unique colour discrimination capabilities despite having one type of photoreceptor, relying instead on chromatic aberration induced by their ocular optics and pupil shapes to perceive spectral information. We took inspiration from this biological mechanism to design a spectral imaging system that combines a ball lens with an event-based camera. Our approach relies on a motorised system that shifts the focal position, mirroring the adaptive lens motion in cephalopods. This approach has enabled us to achieve wavelength-dependent focusing across the visible light and near-infrared spectrum, making the event a spectral sensor. We characterise chromatic aberration effects, using both event-based and conventional frame-based sensors, validating the effectiveness of bio-inspired spectral discrimination both in simulation and in a real setup as well as assessing the spectral discrimination performance. Our proposed approach provides a robust spectral sensing capability without conventional colour filters or computational demosaicing. This approach opens new pathways toward new spectral sensing systems inspired by nature's evolutionary solutions. Code and analysis are available at: https://samiarja.github.io/neuromorphic_octopus_eye/

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