Event cameras reconstruct color from monochrome brightness changes
EventSeeColor: Learning to Reconstruct Color from Monochrome Events
Computer Vision and Pattern RecognitionArtificial Intelligence
Summary
Traditional event cameras only capture changes in brightness, so they produce black-and-white images. The authors show a method to guess what the colors are from these black-and-white event signals. Their method uses both the grayscale images and the original event data together to colorize scenes more accurately. This helps reveal differences in appearance that black-and-white images miss, useful for seeing details in tricky lighting.
What this means in practice
- •For robotics engineers: Enable color perception from monochrome event cameras to improve robot scene understanding under challenging lighting.
- •For mobile camera developers: Develop event-based camera apps that reconstruct color images in low-light or fast-motion conditions where traditional cameras struggle.$Commercial implications: Allows new color event camera products or software for consumer smartphones that work better in difficult lighting.
Authors
Chuanzhi Xu, Langyi Chen, Haoxian Zhou, Zelin Zhang, Haodong Chen, Qiang Qu, Xiaoming Chen, Weidong Cai
Abstract
Event cameras capture visual brightness changes under challenging lighting conditions. However, traditional event-based image reconstruction targets grayscale intensity and produces images without scene color. Color distinguishes surfaces with similar intensity. Reconstructing color from events can reveal appearance differences that reconstructed grayscale images cannot express. Although monochrome event cameras do not directly measure color, their event-triggering patterns depend on surface reflectance, illumination, and the sensor's spectral response. These patterns may contain cues that support color reconstruction. In this paper, we present EventSeeColor, which uses event-reconstructed grayscale and aligned events to jointly guide a diffusion colorizer. Grayscale-derived semantic context supports color inference, while region-balanced chroma supervision and local RGB refinement improve color distribution, brightness, and texture. In the combined evaluation on two real event datasets, EventSeeColor achieves the best structural similarity, learned perceptual scores, and chromatic-region color difference among the external methods.