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Image processing in thermal polarimetry is different from conventional image processing primarily due to the additional polarization information captured by polarimetric sensors. Conventional image processing typically deals with intensity-based images (e.g., grayscale or color images) where each pixel contains information about the intensity or color at a specific location. On the other hand, thermal polarimetry involves processing both the thermal intensity and polarization data to extract valuable information about the scene.


Frenel Imaging has achieved a significant milestone by successfully developing a multi-layered solution empowered with proprietary machine learning. This solution is explicitly designed to tackle the distinct challenges posed by polarimetry multi-layer generated data. The outcome is a framework that produces frames addressing the unique attributes of various applications.

Here are some key differences between image processing in thermal Polarimetry and conventional image processing:

1. Polarimetric Data Representation: In thermal polarimetry, the captured data consists of thermal intensity values and polarization parameters. These parameters include the degree of polarization, angle of polarization, and polarization state (e.g., linear or circular). To process and analyze this combined data, 

2. Polarimetric Calibration: Polarimetric sensors need to be carefully calibrated to ensure accurate measurements of the polarization parameters. Calibration involves determining sensor-specific characteristics, such as the sensor's polarimetric response, sensitivity, and noise characteristics. This calibration step is unique to thermal polarimetry and is not typically required in conventional intensity-based image processing.

3. Data Fusion: In thermal polarimetry, the integration of thermal intensity and polarization data is essential to extract meaningful information. This data fusion step involves combining the different types of information in a coherent manner to enhance image interpretation and improve object detection and identification.

4. Polarization Enhancement Techniques: Conventional image processing techniques focus on enhancing contrast, edge detection, noise reduction, and other features based on intensity variations. In thermal polarimetry, specific polarization enhancement techniques are used to highlight polarized regions, suppress background noise, and improve the visibility of specific materials or objects based on their polarimetric properties.

5. Object Recognition and Material Classification: While conventional image processing may involve object recognition and material classification based on intensity patterns or color information, thermal polarimetry adds an extra dimension to these tasks by incorporating polarization signatures. This can lead to more robust and accurate recognition and classification results, especially in scenarios where thermal and polarimetric properties are crucial for identification.

6. Specific Applications: Thermal polarimetry finds applications in various fields, such as target detection, surveillance, remote sensing, and material analysis, where traditional thermal imaging may not provide enough information for certain tasks. Consequently, image processing algorithms in thermal polarimetry are tailored to address the unique challenges and opportunities presented by these applications.

In summary, thermal polarimetry requires specialized image processing techniques to handle the additional polarization information and extract valuable insights from the combined thermal and polarimetric data. These techniques go beyond conventional image processing methods and play a crucial role in harnessing the full potential of thermal polarimetry in various applications.


4D Image Processing Framework

Proprietary Machine Learning 

Enhanced Thermal image

Temperature correction (angled and emissivity)

Temperature reflection elimination  

Super Resolution 

Classification, Detection and Tracking

anomaly detection 

Application Development Framework


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