The Potential of Diffusion-Based Near-Infrared Image Colorization

Area of Science:

• Computer Vision
• Artificial Intelligence
• Ecology

Background:

• Camera traps are crucial for wildlife monitoring, often using near-infrared (NIR) imaging in low light.
• NIR images lack color information due to differing light reflection properties, creating a perceptual gap.
• Existing colorization methods struggle with NIR data due to domain differences and lack of paired training data.

Purpose of the Study:

• To develop a novel framework for enriching near-infrared images with color using diffusion models.
• To address the challenge of unpaired image-to-image translation for NIR colorization.
• To bridge the domain gap between near-infrared and visible light imaging.

Main Methods:

• Utilized a novel framework based on diffusion models for near-infrared image colorization.
• Explored three implementations of varying complexity for the colorization task.
• Investigated the translation of near-infrared intensities to visible light intensities as a primary control for colorization.

Main Results:

• Demonstrated that diffusion models can effectively colorize near-infrared images.
• Showcased that even a simple visible-near-infrared (VIS-NIR) fusion-inspired method rivals generative adversarial networks (GANs).
• Achieved superior performance compared to GANs with a more complex diffusion model implementation.

Conclusions:

• The proposed diffusion model framework offers an efficient solution for NIR image colorization.
• NIR colorization is significantly influenced by the accurate translation of light intensities.
• This study introduces a new intersection between diffusion models, NIR colorization, and VIS-NIR fusion for ecological applications.