Augmenting full colour-fused multi-band night vision imagery with synthetic imagery in real-time

Toet, Alexander; Hogervorst,; Son, R.J.J.H. van; Dijk, J. C. van

doi:10.1080/19479832.2011.598135

Cited by 15 publications

(15 citation statements)

References 27 publications

(31 reference statements)

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“…The system combines (fuses) the three camera signals in real-time in a false-color RGB signal. It has the capability to perform color remapping using either pre-recorded lookup tables [79] or by deriving the color mapping in real-time from synthetic imagery or Google Earth data [80]. As a result the system provides dynamic imagery with a realistic color appearance in conditions of low visibility (low light levels, smoke).…”

Section: Methodsmentioning

confidence: 99%

The TRICLOBS Dynamic Multi-Band Image Data Set for the Development and Evaluation of Image Fusion Methods

Toet¹,

Hogervorst²,

Pinkus

2016

PLoS ONE

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The fusion and enhancement of multiband nighttime imagery for surveillance and navigation has been the subject of extensive research for over two decades. Despite the ongoing efforts in this area there is still only a small number of static multiband test images available for the development and evaluation of new image fusion and enhancement methods. Moreover, dynamic multiband imagery is also currently lacking. To fill this gap we present the TRICLOBS dynamic multi-band image data set containing sixteen registered visual (0.4–0.7μm), near-infrared (NIR, 0.7–1.0μm) and long-wave infrared (LWIR, 8–14μm) motion sequences. They represent different military and civilian surveillance scenarios registered in three different scenes. Scenes include (military and civilian) people that are stationary, walking or running, or carrying various objects. Vehicles, foliage, and buildings or other man-made structures are also included in the scenes. This data set is primarily intended for the development and evaluation of image fusion, enhancement and color mapping algorithms for short-range surveillance applications. The imagery was collected during several field trials with our newly developed TRICLOBS (TRI-band Color Low-light OBServation) all-day all-weather surveillance system. This system registers a scene in the Visual, NIR and LWIR part of the electromagnetic spectrum using three optically aligned sensors (two digital image intensifiers and an uncooled long-wave infrared microbolometer). The three sensor signals are mapped to three individual RGB color channels, digitized, and stored as uncompressed RGB (false) color frames. The TRICLOBS data set enables the development and evaluation of (both static and dynamic) image fusion, enhancement and color mapping algorithms. To allow the development of realistic color remapping procedures, the data set also contains color photographs of each of the three scenes. The color statistics derived from these photographs can be used to define color mappings that give the multi-band imagery a realistic color appearance.

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Section: Methodsmentioning

confidence: 99%

The TRICLOBS Dynamic Multi-Band Image Data Set for the Development and Evaluation of Image Fusion Methods

Toet¹,

Hogervorst²,

Pinkus

2016

PLoS ONE

Self Cite

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“…The process differs from system to system but often input images are fused into a false color image. Displaying three-band natural color images has been the goal of some recent approaches, and color transfer using a reference color image such as a synthetic, daytime, or any other image source has been a common solution [1][2][3][4][5][6].…”

Section: Introductionmentioning

confidence: 99%

Color night vision system for ground vehicle navigation

Qadir

Kozaitis

2014

SPIE Proceedings

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Operating in a degraded visual environment due to darkness can pose a threat to navigation safety. Systems have been developed to navigate in darkness that depend upon differences between objects such as temperature or reflectivity at various wavelengths. However, adding sensors for these systems increases the complexity by adding multiple components that may create problems with alignment and calibration. An approach is needed that is passive and simple for widespread acceptance. Our approach uses a type of augmented display to show fused images from visible and thermal sensors that are continuously updated. Because the raw fused image gave an unnatural color appearance, we used a color transfer process based on a look-up table to replace the false colors with a colormap derived from a daytime reference image obtained from a public database using the GPS coordinates of the vehicle. Although the database image was not perfectly registered, we were able to produce imagery acquired at night that appeared with daylight colors. Such an approach could improve the safety of nighttime navigation.

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“…superresolution, local-contrast enhancement, noise reduction), and the combination of the images with synthetic databases 10,12 . Also, the imagery can be shared with others to create better situational awareness.…”

mentioning

confidence: 99%

Colour-the-INSight: combining a direct view rifle sight with fused intensified and thermal imagery

et al. 2012

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We present the design and evaluation of a new demonstrator rifle sight viewing system containing direct view, red aim point and fusion of an (uncooled, LWIR) thermal sensor with a digital image intensifier. Our goal is to create a system that performs well under a wide variety of (weather) conditions during daytime and nighttime and combines the advantages of the various sensor systems. A real-time colour image with salient hot targets is obtained from the night vision sensors by implementing the Colour-the-Night fusion method (Hogervorst & Toet, 2010) on the on-board processor. The prototype system was evaluated in a series of field trials with military observers performing detection and identification tasks. The tests showed that during daytime the addition of a thermal image to direct vision is advantageous, e.g. for the detection of hot targets. At nighttime, the fusion of thermal and image intensified imagery results in increased situational awareness and improved detection of (hot) targets. For identification of small (handheld) objects, the technology needs to be further refined

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Augmenting full colour-fused multi-band night vision imagery with synthetic imagery in real-time

Cited by 15 publications

References 27 publications

The TRICLOBS Dynamic Multi-Band Image Data Set for the Development and Evaluation of Image Fusion Methods

The TRICLOBS Dynamic Multi-Band Image Data Set for the Development and Evaluation of Image Fusion Methods

Color night vision system for ground vehicle navigation

Colour-the-INSight: combining a direct view rifle sight with fused intensified and thermal imagery

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