Super-resolution enhancement of night vision image sequences

Sale, Deepali; Schultz, Richard R.; Szczerba, R.J.

doi:10.1109/icsmc.2000.886256

Cited by 11 publications

(9 citation statements)

References 9 publications

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“…These 120 target images used every combination of target type (small, medium, and large, 10 targets in total), level of masking (low, medium and high) and quadrant location (1)(2)(3)(4). For each target-masking combination, a different target orientation (45, 135, 225 or 315 degrees) was randomly assigned for each of the four quadrants.…”

Section: Stimulimentioning

confidence: 99%

<title>Visual performance-based image enhancement methodology: an investigation of three Retinex algorithms</title>

et al. 2005

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While vast numbers of image enhancing algorithms have already been developed, the majority of these algorithms have not been assessed in terms of their visual performance-enhancing effects using militarily relevant scenarios. The goal of this research was to develop a visual performance-based assessment methodology and apply it to assess three Retinex algorithms. The image enhancing algorithms used in this study are the two algorithms described in Funt, Ciurea, and McCann 1 as McCann99 Retinex and Frankle-McCann Retinex, and the multiscale Retinex with color restoration (MSRCR) 2 algorithm. This paper discusses the methodology developed to acquire objective human visual performance data as a means of evaluating various image enhancement algorithms. The basic approach is to determine whether or not standard objective performance metrics, such as response time and error rate, are improved when viewing the enhanced images versus the baseline, non-enhanced images. Four observers completed a visual search task using a spatial-forcedchoice paradigm. Observers had to search images for a target (a military vehicle) hidden among foliage and then indicate in which quadrant of the screen the target was located. Response time and percent correct were measured for each observer. Future directions and the viability of this technique are also discussed.

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Section: Stimulimentioning

confidence: 99%

<title>Visual performance-based image enhancement methodology: an investigation of three Retinex algorithms</title>

et al. 2005

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“…(iii) Surveillance systems [94], where SR is used to increase the quality in video surveillance systems, using such recorded sequences as forensic digital video, and even to be admitted as evidence in the courts of law. SR improves night vision systems when images have been acquired with infrared sensors [95] and helps in the face recognition process for security purposes [96]. (iv) Medical image acquisition [97].…”

Section: Sr Advancesmentioning

confidence: 99%

Advances in video coding for hand-held device implementation in networked electronic media

Núñez

2006

J Real-Time Image Proc

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This survey addresses a number of challenges and research areas identified in real time image processing for state-of-the-art hand-held device implementation in networked electronic media. The challenges appear when having to develop and map processing algorithms not only on fading, noisy, and multi-path band limited transmission channels, but more specifically here on the limited resources available for decoding and scalable rendering on batterylimited hand-held mobile devices. Networked electronic media requires scalable video coding which in turn introduces additional degradation. These problems raise some complex issues discussed in the paper. A need to extend, modify and even create new algorithms and tools, targeting architectures, technology platforms, and design techniques as well as scalability, computational load and energy efficiency considerations has established itself as a key research area. A multidisciplinary approach is advocated.

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“…Night video enhancement [1][2][3][4][5][6] is one of the most important and difficult component of video security surveillance system. The increasing use of night operations requires more details and integrated information from the enhanced image.…”

Section: Introductionmentioning

confidence: 99%

“…Those methods can be roughly classified as two categories [7] : spatial-domain processing and compressed-domain processing. The spatial-domain methods operate directly on image pixels [3][4][5] . The compressed-domain methods [6] operate directly on the transform coefficients of the images that are compressed, for example, by Fourier, wavelet, or discrete cosine transforms.…”

Section: Introductionmentioning

confidence: 99%

Combining scene model and fusion for night video enhancement

Yang

Chen

2009

J. Electron.(China)

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This paper presents a video context enhancement method for night surveillance. The basic idea is to extract and fuse the meaningful information of video sequence captured from a fixed camera under different illuminations. A unique characteristic of the algorithm is to separate the image context into two classes and estimate them in different ways. One class contains basic surrounding scene information and scene model, which is obtained via background modeling and object tracking in daytime video sequence. The other class is extracted from nighttime video, including frequently moving region, high illumination region and high gradient region. The scene model and pixel-wise difference method are used to segment the three regions. A shift-invariant discrete wavelet based image fusion technique is used to integral all those context information in the final result. Experiment results demonstrate that the proposed approach can provide much more details and meaningful information for nighttime video.

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Super-resolution enhancement of night vision image sequences

Cited by 11 publications

References 9 publications

<title>Visual performance-based image enhancement methodology: an investigation of three Retinex algorithms</title>

<title>Visual performance-based image enhancement methodology: an investigation of three Retinex algorithms</title>

Advances in video coding for hand-held device implementation in networked electronic media

Combining scene model and fusion for night video enhancement

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