Signal conditioning algorithms for enhanced tactical sensor imagery

Schutte, Klamer; Lange, Dirk-Jan J. de; Broek, Sebastian P. van den

doi:10.1117/12.487720

Cited by 30 publications

(33 citation statements)

References 4 publications

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“…Dynamic signal processing techniques such as super-resolution reconstruction (SR) or scene-based non-uniformity correction (SBNUC) and many others have been widely reported in the literature 1,2,3,8 . These techniques make use of motion in the image in order to improve pixel resolution and/or to reduce temporal noise (SR) in an undersampled system or to improve contrast sensitivity (reduce the fixed pattern noise in real time) by comparing the responses from one pixel to the other using the scene (SBNUC).…”

Section: Dynamic Signal Processing Techniquesmentioning

confidence: 99%

“…Other applications are signal processing techniques such as Super Resolution 1,2 or Scene-Based NonUniformity Correction 2 . These techniques now become of practical importance since they nowadays can be applied in real-time 3 . With hand-held systems, range performance can be higher when operated hand-held than from a tripod 9. These developments inevitably ask for an end-to-end measure that is able to quantify dynamic sensor performance or system performance including signal processing, and to compare this performance to that under conventional static imaging.…”

Section: Introductionmentioning

confidence: 99%

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Applicability of TOD, MTDP, MRT and DMRT for dynamic image enhancement techniques

Bijl¹,

Schutte²,

Hogervorst³

2006

Infrared Imaging Systems: Design, Analysis, Modeling, and Testing XVII

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Current end-to-end sensor performance measures such as the TOD, MRT, DMRT and MTDP were developed to describe Target Acquisition performance for static imaging. Recent developments in sensor technology (e.g. microscan) and image enhancement techniques (e.g. Super Resolution and Scene-Based Non-Uniformity Correction) require that a sensor performance measure can be applied to dynamic imaging as well. We evaluated the above-mentioned measures using static, dynamic (moving) and different types of enhanced imagery of thermal 4-bar and triangle tests patterns. Both theoretical and empirical evidence is provided that the bar-pattern based methods are not suited for dynamic imaging. On the other hand, the TOD method can be applied easily without adaptation to any of the abovementioned conditions, and the resulting TOD data are in correspondence with the expectations. We conclude that the TOD is the only current end-to-end measure that is able to quantify sensor performance for dynamic imaging and dynamic image enhancement techniques.

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Section: Dynamic Signal Processing Techniquesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Applicability of TOD, MTDP, MRT and DMRT for dynamic image enhancement techniques

Bijl¹,

Schutte²,

Hogervorst³

2006

Infrared Imaging Systems: Design, Analysis, Modeling, and Testing XVII

Self Cite

View full text Add to dashboard Cite

show abstract

“…Amplifying the small contrast variations achieves a higher, evenly divided contrast for the entire image. [3][4][5] Finally, imagery may undergo a shift in hue with respect to the original scene due to the filtering of the water. A color correction algorithm restores the original colors of the scenario as well as possible.…”

Section: A Novel Filter That Enhances Underwater Video Footage In Remmentioning

confidence: 99%

Image processing provides better views under water

Kemp¹,

Dijk²,

Schutte³

et al. 2014

SPIE Newsroom

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A novel filter that enhances underwater video footage in remotely operated vehicle systems is well suited to both military and civilian applications.When looking under water with a camera, the user is hampered by low contrast, color changes, and interference due, for example, to camera noise and floating particles such as sea snow. Especially with cameras mounted on remotely operated vehicle (ROV) systems, common in both military and civil applications, these effects make observation more difficult and tiring. Applying image enhancement significantly eases the task of observation. Here, we describe a software solution for enhancing underwater imagery. Wherever possible, we have reused algorithms based on earlier developments in the area of compound security 1 and UAV (unmanned aerial vehicle) image 2 enhancement, and have transformed and extended them for this underwater case. Our method comprises three techniques.A smart temporal average filter called noise reduction simultaneously reduces noise and sea snow. Noise reduction requires first estimating the camera motion. While performing this subpixel estimation, adaptive image integration reduces temporal noise, which in turn decreases sea snow. Further details on image registration for enhancement purposes are available elsewhere. 1 Underwater imagery may also suffer from low contrast, especially in murky waters and when observing over larger distances. Amplifying the small contrast variations achieves a higher, evenly divided contrast for the entire image. [3][4][5] Finally, imagery may undergo a shift in hue with respect to the original scene due to the filtering of the water. A color correction algorithm restores the original colors of the scenario as well as possible. This algorithm is applied prior to the display of the imagery. Note that color correction must be designed from scratch for the specific underwater case. 6 Many hardware solutions for contrast enhancement exist. A main drawback of such methods is that applying only contrast enhancement will also amplify camera noise and sea snow. We have implemented the combination of noise reduction, local adaptive contrast enhancement, and color correction on a commercial off-the-shelf (COTS) laptop used to control an ROV equipped with an NVIDIA Compute Unified Device Architecture (CUDA)-capable graphics processing unit (GPU). The local adaptive contrast enhancement and color correction are applied by the GPU and the noise reduction by the central processing unit (CPU). The result is displayed directly on the laptop. However, in other implementations alternative choices can be made for the hardware. Figure 1 shows the flowchart of our method.We developed and tested our approach in an operational navy environment, in collaboration with Dutch Ministry of Defence ROV operators. For the development and tuning of the algorithms, we used reference video footage, acquired in the murky waters of the Netherlands. We tested the software using data from other but similar underwater scenarios. Figure 2 shows snapshots o...

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“…This software approach is based on earlier developed image enhancement tools as described in [23,20]. First, the images in the original sequence are registered to a reference frame.…”

Section: Global-based Software Approachmentioning

confidence: 99%

An overview of turbulence compensation

et al. 2012

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In general, long range visual detection, recognition and identification are hampered by turbulence caused by atmospheric conditions. Much research has been devoted to the field of turbulence compensation. One of the main advantages of turbulence compensation is that it enables visual identification over larger distances. In many (military) scenarios this is of crucial importance. In this paper we give an overview of several software and hardware approaches to compensate for the visual artifacts caused by turbulence. These approaches are very diverse and range from the use of dedicated hardware, such as adaptive optics, to the use of software methods, such as deconvolution and lucky imaging. For each approach the pros and cons are given and it is indicated for which type of scenario this approach is useful. In more detail we describe the turbulence compensation methods TNO has developed in the last years and place them in the context of the different turbulence compensation approaches and TNO's turbulence compensation roadmap. Furthermore we look forward and indicate the upcoming challenges in the field of turbulence compensation.

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Signal conditioning algorithms for enhanced tactical sensor imagery

Cited by 30 publications

References 4 publications

Applicability of TOD, MTDP, MRT and DMRT for dynamic image enhancement techniques

Applicability of TOD, MTDP, MRT and DMRT for dynamic image enhancement techniques

Image processing provides better views under water

An overview of turbulence compensation

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