Search

We present the design and first field trial results of the all-day all-weather INVIS Integrated Night Vision surveillance and observation System. The INVIS augments a dynamic three-band false-color nightvision image with synthetic 3D imagery in a real-time display. The night vision sensor suite consists of three cameras, respectively sensitive in the visual (400-700 nm), the near-infrared (700-1000 nm) and the longwave infrared (8-14 μm) bands of the electromagnetic spectrum. The optical axes of the three cameras are aligned. Image quality of the fused sensor signals can be enhanced in real-time through Dynamic Noise Reduction, Superresolution, and Local Adaptive Contrast Enhancement. The quality of the longwave infrared image can be enhanced through Scene-Based Non-Uniformity Correction (SBNUC), intelligent clustering and thresholding. The visual and near-infrared signals are used to represent the resulting multiband nightvision image in realistic daytime colors, using the Color-the-Night color remapping principle. Color remapping can also be deployed to enhance the visibility of thermal targets that are camouflaged in the visual and near-infrared range of the spectrum. The dynamic false-color nighttime images can be augmented with corresponding synthetic 3D scene views, generated in real-time using a geometric 3D scene model in combination with position and orientation information supplied by the GPS and inertial sensors of the INVIS system. © 2010 SPIE.

We present the design and first field trial results of the INVIS integrated night vision surveillance and observation system, in particular for the image enhancement techniques implemented. The INVIS is an all-day-andnight all-weather navigation and surveillance tool, combining three-band cameras. We present a processing pipeline for this system. The image quality of all individual sensor signals is enhanced through Dynamic Noise Reduction and Dynamic Super Resolution. The quality of the thermal image can be enhanced through Scene-Based Non-Uniformity Correction (SBNUC). The images are fused using natural tone mapping techniques. The contrast in the image can be improved using Local Adaptive Contrast Enhancement, applied before or after the tone mapping. These results show that the image enhancement techniques have an added value for image fusion systems. © 2010 SPIE.

We present the design and first field trial results of the INVIS integrated night vision surveillance and observation system, in particular for the image enhancement techniques implemented. The INVIS is an all-day-andnight all-weather navigation and surveillance tool, combining three-band cameras. We present a processing pipeline for this system. The image quality of all individual sensor signals is enhanced through Dynamic Noise Reduction and Dynamic Super Resolution. The quality of the thermal image can be enhanced through Scene-Based Non-Uniformity Correction (SBNUC). The images are fused using natural tone mapping techniques. The contrast in the image can be improved using Local Adaptive Contrast Enhancement, applied before or after the tone mapping. These results show that the image enhancement techniques have an added value for image fusion systems. © 2010 SPIE.

We present the design and first field trial results of the all-day all-weather INVIS Integrated Night Vision surveillance and observation System. The INVIS augments a dynamic three-band false-color nightvision image with synthetic 3D imagery in a real-time display. The night vision sensor suite consists of three cameras, respectively sensitive in the visual (400-700 nm), the near-infrared (700-1000 nm) and the longwave infrared (8-14 μm) bands of the electromagnetic spectrum. The optical axes of the three cameras are aligned. Image quality of the fused sensor signals can be enhanced in real-time through Dynamic Noise Reduction, Superresolution, and Local Adaptive Contrast Enhancement. The quality of the longwave infrared image can be enhanced through Scene-Based Non-Uniformity Correction (SBNUC), intelligent clustering and thresholding. The visual and near-infrared signals are used to represent the resulting multiband nightvision image in realistic daytime colors, using the Color-the-Night color remapping principle. Color remapping can also be deployed to enhance the visibility of thermal targets that are camouflaged in the visual and near-infrared range of the spectrum. The dynamic false-color nighttime images can be augmented with corresponding synthetic 3D scene views, generated in real-time using a geometric 3D scene model in combination with position and orientation information supplied by the GPS and inertial sensors of the INVIS system. © 2010 SPIE.

This thesis describes and evaluates a number of algorithms for reducing fixed pattern noise in image sequences. Fixed pattern noise is the dominantnoise component for many infrared detector systems, perceived as a superimposed pattern that is approximately constant for all image frames. Primarily, methods based on estimation of the movement between individual image frames are studied. Using scene-matching techniques, global motion between frames can be successfully registered with sub-pixel accuracy. This allows each scene pixel to be traced along a path of individual detector elements. Assuming a static scene, differences in pixel intensities are caused by fixed pattern noise that can be estimated and removed. The algorithms have been tested by using real image data from existing infrared imaging systems with good results. The tests include both a two-dimensional focal plane array detector and a linear scanning one-dimensional detector, in different scene conditions.