How do you combine images from different modalities, such as optical, thermal, and radar?

Image fusion involves three main steps: image registration, image transformation, and image combination. Image registration aligns images from different sources to the same coordinate system using features or landmarks. Image transformation converts the images to a suitable format, like spatial, frequency, or wavelet domains. Finally, image combination merges the transformed images using rules like pixel averaging, maximum selection, or weighted fusion.

This is delicate work! First of all, you need to ensure that the imagery data are geometrically consistent. If they are not, you will need to perform registration and transformation. Next, you need to decide how to visualize the data. This is a somewhat subjective matter. For example, let's say you have two images in visible and medium infrared bands showing the same scene. In thermal imaging, we are generally interested in objects with high thermal signatures, such as a vehicle camouflaged by a bush. Here, you should design your fusion strategy so that the object of interest is clearly shown in the fused image

Image fusion combines multiple images into one. There are several methods for this. Pixel-level fusion combines raw pixel values using simple arithmetic or logical operations; it's fast but might not keep important details. Feature-level fusion extracts and combines meaningful features using decision trees, neural networks, or fuzzy logic; it captures high-level information but is complex and computationally intensive. Decision-level fusion involves inference or classification on the images and combines the results using strategies like voting or Bayesian inference; it's robust and reliable but depends on the quality of the individual methods.

One of the challenges in fusing disparate modalities such as an optical sensor and an imaging radar is the asynchronous nature of the data. A full motion video sensor may output 30 frames per second or more, while an imaging radar might take up to 30 seconds just to generate a single image.

Just to correct Jozsef, the situation where you have asynchronous and disparate data is precisely what Dempster-Schaeffer is good for, the original article just doesn't mention this important property of the algorithm for some reason.

Image fusion faces several challenges, including image quality, compatibility, and trade-offs. Quality assessment and enhancement techniques are often needed to improve the input images. Compatibility measures ensure the images are properly aligned and represented. Additionally, fusion criteria and parameters may need adjustment or optimization to balance trade-offs and achieve the desired result.

Temporal noise reduction is a classic example of image fusion by processing pixels captured at different time. The feature of face de-blur on Google Pixel phone camera is another example by fusing images captured from two cameras of different field of view to increase image clarity.

Image fusion offers several benefits, such as improved contrast, resolution, clarity, and detail, enhancing visual perception and interpretation. The fused image contains more information and features than individual images, enriching its content and meaning. Additionally, it has less noise, ambiguity, and inconsistency, reducing uncertainty and error.

Image fusion combines images from different modalities to create a single image. For example, fusing an optical and thermal image of a building reveals both visible and thermal features. Combining an optical and radar image of a forest integrates optical and radar information. Similarly, merging an optical and MRI image of a brain integrates anatomical, functional, blood flow, and pathological data.

Different sensors types observe the scene using different domains. For example a camera is good at angular resolution and colour, but poor in range and velocity. A Doppler radar is good in range and velocity, but poor with angular resolution. Sensor fusion needs to understand the different domains to get the best out of it.

Image fusion is not just a technical process but also has practical applications in various fields. For instance, in medical diagnostics, fused images can help doctors detect diseases more accurately by combining different imaging techniques. In remote sensing, fused satellite images can provide better land use and environmental monitoring. Additionally, in security and surveillance, fused images from different sensors can improve the detection and identification of objects or people. These examples highlight the real-world impact and value of image fusion in enhancing decision-making and outcomes across different industries.