Thermal V-Curve Fusion Image Decluttering
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Solution Overview
Problem
Conventional night vision fusion systems face cluttered image issues due to the combination of image intensification and thermal sensing data, making it difficult for operators to distinguish important details as both sensors compete for visual space on every pixel.
Innovation Solution
A method that adjusts and transforms the contrast of both visual and thermal images using a V-curve, assigning different hues based on temperature, and applying local area contrast enhancement to improve visibility by setting ambient temperature to minimum brightness and hottest/coldest pixels to maximum brightness, allowing better distinction between thermal and visual imagery.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If both image intensification and thermal sensing data are combined in a fused image, then additional thermal information is provided to the operator, but the image becomes cluttered and important details become difficult to distinguish
Solution Approach 1:
The patent applies different processing strategies to different temperature regions of the thermal image. Hot regions (above threshold) display full thermal imagery while cold regions (below threshold) are suppressed or removed. This local differentiation resolves the contradiction by providing thermal information where needed (hot regions) while eliminating clutter where it harms viewability (cold regions).
Solution Approach 2:
The patent dynamically adjusts the threshold temperature parameter based on environmental conditions and operator needs. By changing this parameter, the system can adaptively control the amount of thermal information displayed versus the visual clarity maintained, resolving the contradiction between information provision and image quality.
2Ease of operation
If overlay mode is used to show only LWIR imagery above a predetermined threshold temperature, then clutter in cold regions is reduced, but clutter remains in hot regions
Solution Approach 1:
Instead of the conventional overlay mode that shows thermal data above a threshold, the patent inverts the approach by showing thermal data below a threshold while suppressing data above the threshold. This inversion resolves the contradiction by eliminating hot region clutter (the problematic area in conventional systems) while preserving cold region thermal information.
Solution Approach 2:
The patent applies different visibility rules to different temperature zones: cold regions maintain thermal imagery visibility while hot regions have thermal imagery suppressed. This localized quality assignment resolves the contradiction by tailoring the information display to the specific needs of each temperature zone.
3Loss of information
If both sensors compete for visual space on every pixel, then complete sensor data is displayed, but the viewer cannot distinguish important details in one of the sensors
Solution Approach 1:
The patent extracts and removes thermal imagery data from regions where it competes with and obscures visual imagery details. By taking out the thermal data from hot regions where visual details are most critical, the system preserves overall data completeness while eliminating interference with detail distinguishability in key areas.
Solution Approach 2:
The patent segments the image display into different temperature-based zones with different processing rules. This segmentation allows complete sensor data to be processed and displayed in appropriate regions while preventing competition for visual space in regions where detail distinguishability is paramount.
Data Source
AI summary
A method of fusing two images includes adjusting contrast of (a) a visual image and (b) a thermal image. Also included is modifying the adjusted contrast of the thermal image to form output brightness levels, by using a transformation curve, and then displaying both (a) the brightness levels of the thermal image and (b) the adjusted contrast of the visual image. Modifying the adjusted contrast of the thermal image includes using a V-curve as the transformation curve, in which the V-curve includes similar maximum output brightness levels at the coldest and hottest relative temperatures, respectively. The V-curve also includes a minimum output brightness level at a midpoint between the coldest and hottest relative temperatures. Furthermore, the method assigns different hues to the intensity values of the thermal image, ranging from coldest to hottest relative temperatures.