Adaptive image enhancement based on clustering of wavelet coefficients for infrared sea surveillance systems

Karali, A. Onur; Okman, O. Erman; Aytaç, Tayfun

doi:10.1016/j.infrared.2011.05.003

Cited by 23 publications

(12 citation statements)

References 34 publications

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“…If = 20, = 200, = [3,3], the output image in weak sun glints scene is good, but the result of strong sun glints scene is worse and there is lots of noise signal in the image. On the contrary, if = 200, = 20, = [3,3], the details of the ship targets is blurry, but the result in strong glints scene is better than the former case. Therefore, it is necessary to make and adjusted dynamically.…”

Section: Adaptive Bilateral Filteringmentioning

confidence: 99%

“…where is the unsharp mask region and its size is [3,3], ( , ) ̅̅̅̅̅̅̅̅ is the mean value in the mask region. The base layer can be mapped from the following equation.…”

Section: Adaptive Bilateral Filteringmentioning

confidence: 99%

“…To the best of our knowledge, Karali et al have developed an IR image enhancement technique specific for sea surface targets [2] [3] . But Karali et al's method is not able to adapt to different scenes and gives general performance with our image data.…”

Section: Introductionmentioning

confidence: 99%

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Adaptive enhancement of sea surface targets in infrared high dynamic range image

Dong

Lin

2015

SPIE Proceedings

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A high dynamic range infrared image of the sea surface scene includes the effects due to the sea clutter, mirror reflections from the wave facets, which decrease the visibility of the ship targets and their details. This paper provides an efficient adaptive enhancement technique for ship targets based on bilateral filtering and visual saliency detection. The 14-bit raw image is separated into a detail layer and a base layer by applying an adaptive bilateral filter. Then the two layers are processed separately and added afterward. Hereafter by employing visual saliency detection we can get the gain matrix to improve the contrast of ship targets. Finally, the image whose contrast is improved is quantized to the display range. The strength of our proposed method lies in its ability to inhibit the sea clutter and adaptability in different sea surface scene and shows a better performance in the contrast of the ship targets and the visibility of their details.

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Section: Adaptive Bilateral Filteringmentioning

confidence: 99%

“…where is the unsharp mask region and its size is [3,3], ( , ) ̅̅̅̅̅̅̅̅ is the mean value in the mask region. The base layer can be mapped from the following equation.…”

Section: Adaptive Bilateral Filteringmentioning

confidence: 99%

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Adaptive enhancement of sea surface targets in infrared high dynamic range image

Dong

Lin

2015

SPIE Proceedings

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“…Military applications includes target acquisition, surveillance, homing and tracking, and night vision, while non-military applications includes public surveillance, remote temperature sensing, thermal efficiency analysis, weather forecasting, spectroscopy, and environmental monitoring [1] [2]. In recent years, IR systems have also attracted much attention in maritime security and traffic monitoring [3]. In this domain segmentation of IR ship images is one of the key and challenging step which guides the following recognition and tracking tasks.…”

Section: Introductionmentioning

confidence: 99%

Saliency based algorithm for ship detection in infrared images

Mumtaz¹,

Jabbar

Mahmood³

et al. 2016

2016 13th International Bhurban Conference on Applied Sciences and Technology (IBCAST)

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In this paper we have worked on the problem of automatic ship detection in IR images. Segmentation of IR ship images is always a challenging task because of the intensity inhomogeneity, sea clutters and noise. In our proposed approach, we have shown that efficiency and accuracy of IR ship detection algorithms can be enhanced by only searching around the salient parts of the input image. In order to identify most salient regions, at first we computed the saliency map of the input image using the Graph-Based Visual Saliency (GBVS) algorithm. Next a multilevel thresholding of the saliency map is performed to get the ranked salient regions of the input image. By using the ship size as prior information, top-k regions are further processed to get the fine segmentation of the target. For this purpose we have used spatial constraint based fuzzy c-mean (FCM) segmentation algorithm along with a strategy to choose the cluster selection threshold. Experiments are performed on a data set of 18 diverse and challenging IR ship images, collected from different sources. Results show that our proposed framework is very effective and perform better compare to the methods which directly search the target in entire image.

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“…It has a wide range of applications in marine security. Generally, an IR image of sea surface reflects the effects of sky reflections, black-body emissions from wave facets and atmosphere, sea clutter, and imaging distance [1]. Also, limited by IR camera technology, the IR ship targets are generally characterized with low signal-to-noise ratio (SNR) and low target-to-background contrast [2].…”

Section: Introductionmentioning

confidence: 99%

Infrared ship target segmentation based on region and shape features

Liu¹,

Zhou²,

Bai³

2013

2013 14th International Workshop on Image Analysis for Multimedia Interactive Services (WIAMIS)

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We explore problem of ship target segmentation in infrared (IR) images, and propose an efficient method. The method consists of two procedures: first, based on the intensity properties of target and background region, we design a global background subtraction filter (GBSF) to suppress the background, and an adaptive row mean subtraction filter (ARMSF) to enhance the target. By iteratively applying the two filters, the target region could be separated from the background. Then, considering shape features of the ship targets, we propose a target selection method to get the final ship target. Experimental results show that, the presented method could effectively segment ship targets from IR images with different sea background. It performs better in both the visual and quantitative comparisons, especially for IR images with low contrast.

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Adaptive image enhancement based on clustering of wavelet coefficients for infrared sea surveillance systems

Cited by 23 publications

References 34 publications

Adaptive enhancement of sea surface targets in infrared high dynamic range image

Adaptive enhancement of sea surface targets in infrared high dynamic range image

Saliency based algorithm for ship detection in infrared images

Infrared ship target segmentation based on region and shape features

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