RollingPlain/IVIF_ZOO

What it solves

IVIF Zoo is a comprehensive resource hub designed to accelerate research and development in Infrared and Visible Image Fusion (IVIF). It addresses the challenge of combining data from infrared sensors (which capture thermal signatures) and visible light sensors (to capture texture and detail) into a single image that preserves the most critical information from both modalities.

How it works

The repository acts as a central directory and benchmark for IVIF, providing:

• Curated Datasets: A collection of image and video datasets (such as TNO, RoadScene, M3FD, and VF-Bench) with detailed specifications on resolution and annotations.
• Methodology Catalog: A categorized list of fusion methods based on their architecture (Auto-Encoders, GANs, CNNs) and their primary goal (Visual Enhancement, Data Compatibility, or Application-oriented).
• Evaluation Metrics: Tools and references for measuring the fusion quality.
• Resource Library: Downloadable results for fusion, segmentation, and detection tasks based on established models like SegFormer and YOLO-v5.

Who it’s for

This project is intended for researchers, engineers, and computer vision enthusiasts who are working on multi-modal image processing, thermal imaging, and downstream visual perception tasks like object detection and semantic segmentation.

Highlights

• Extensive Dataset Directory: Includes both static image pairs and video sequences for comprehensive testing.
• Taxonomy of Methods: Organizes a wide array of state-of-the-art fusion techniques from various academic venues (TIP, CVPR, NeurIPS).
• Task-Oriented Approach: Focuses not only on visual quality but also on how fusion affects downstream tasks like object detection and segmentation.
• Integrated Resource Library: Provides direct access to results and benchmarks for various IVIF methods.