Infrared Colorization Using Deep Convolutional Neural Networks
This paper proposes a method for transferring the RGB color spectrum to near-infrared (NIR) images using deep multi-scale convolutional neural networks. A direct and integrated transfer between NIR and RGB pixels is trained. The trained model does not require any user guidance or a reference image database in the recall phase to produce images with a natural appearance. To preserve the rich details of the NIR image, its high frequency features are transferred to the estimated RGB image. The presented approach is trained and evaluated on a real-world dataset containing a large amount of road scene images in summer. The dataset was captured by a multi-CCD NIR/RGB camera, which ensures a perfect pixel to pixel registration.
Code (2)
Tasks
ColorizationSimilar Papers 제목 키워드 기반
FSCM: Frequency-Enhanced Spatial-Spectral Coupled Mamba for Infrared Hyperspectral Image Colorization
Thermal infrared imaging is robust to illumination variations and smoke interference, making it important for all-weather perception. However, the lack of natural color and fine texture limits target recognition, human v…
Semantic SegmentationImage ColorizationMTSIC: Multi-stage Transformer-based GAN for Spectral Infrared Image Colorization
Thermal infrared (TIR) images, acquired through thermal radiation imaging, are unaffected by variations in lighting conditions and atmospheric haze. However, TIR images inherently lack color and texture information, limi…
ColorizationGenerative Adversarial NetworkImage ColorizationMemory-Guided Collaborative Attention for Nighttime Thermal Infrared Image Colorization
Nighttime thermal infrared (NTIR) image colorization, also known as translation of NTIR images into daytime color images (NTIR2DC), is a promising research direction to facilitate nighttime scene perception for humans an…
ColorizationGenerative Adversarial NetworkImage ColorizationImage-to-Image Translation+3Thermal Infrared Image Colorization for Nighttime Driving Scenes with Top-Down Guided Attention
Benefitting from insensitivity to light and high penetration of foggy environments, infrared cameras are widely used for sensing in nighttime traffic scenes. However, the low contrast and lack of chromaticity of thermal …
ColorizationImage ColorizationTranslationAutomatic Video Colorization using 3D Conditional Generative Adversarial Networks
In this work, we present a method for automatic colorization of grayscale videos. The core of the method is a Generative Adversarial Network that is trained and tested on sequences of frames in a sliding window manner. N…
ColorizationGenerative Adversarial Network