Abstract:
"Colorizing near-infrared (NIR) images poses
unique challenges due to the absence of color information and
the nuances in light absorption. In this paper, we present a novel
approach to NIR image colorization utilizing a synthetic dataset
generated from visible light images. Our method addresses two
major challenges encountered in NIR image colorization:
accurately colorizing objects with color variations and avoiding
over/under saturation in dimly lit scenes.
To tackle these challenges, we propose a Generative
Adversarial Network (GAN)-based framework that learns to
map NIR images to their corresponding colorized versions. The
synthetic dataset ensures diverse color representations,
enabling the model to effectively handle objects with varying
hues and shades. Furthermore, the GAN architecture facilitates
the generation of realistic colorizations while preserving the
integrity of dimly lit scenes, thus mitigating issues related to
over/under saturation.
Experimental results on benchmark NIR image datasets
demonstrate the efficacy of our approach in producing high
quality colorizations with improved color accuracy and
naturalness. Quantitative evaluations and comparative studies
validate the superiority of our method over existing techniques,
showcasing its robustness and generalization capability across
diverse NIR image scenarios.
Our research not only contributes to advancing NIR image
colorization but also underscores the importance of synthetic
datasets and GANs in addressing domain-specific challenges in
image processing tasks. The proposed framework holds
promise for various applications in remote sensing, medical
imaging, and surveillance where accurate color representation
of NIR imagery is crucial for analysis and interpretation."
