Variance WIE based infrared images processing

Yang, Lei; Zhou, Yi; Yang, Jie; Chen, L.

doi:10.1049/el:20060827

Cited by 33 publications

(22 citation statements)

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“…The variance WEI is a statistical form of the characteristics, which reflects the average information of a figure. It was first used to detect infrared images [20].…”

Section: Improved Visual Attention Modelmentioning

confidence: 99%

SAR image target detection in complex environments based on improved visual attention algorithm

Liu

Cao

2014

J Wireless Com Network

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A novel target detection algorithm for synthetic aperture radar (SAR) images based on an improved visual attention method is proposed in this paper. With the development of SAR technology, target detection algorithms are confronted with many difficulties such as a complicated environment and scarcity of target information. Visual attention of the human visual system can make humans easily focus on key points in a complex picture, and the visual attention algorithm has been used in many fields. However, existing algorithms based on visual attention models cannot obtain satisfactory results for SAR image target detection under complex environmental conditions. After analysing the existing visual attention models, we combine the pyramid model of visual attention with singular value decomposition to simulate the human retina, which can make the visual attention model more suitable to the characteristics of SAR images. We introduce variance weighted information entropy into the model to optimize the detection results. The results obtained by the existing visual attention algorithm for target detection in SAR images yield a large number of false alarms and misses. However, the proposed algorithm can improve both the efficiency and accuracy of target detection in a complicated environment and under weak-target conditions. The experimental results validate the performance of our method.

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“…The variance WEI is a statistical form of the characteristics, which reflects the average information of a figure. It was first used to detect infrared images [20].…”

Section: Improved Visual Attention Modelmentioning

confidence: 99%

SAR image target detection in complex environments based on improved visual attention algorithm

Liu

Cao

2014

J Wireless Com Network

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“…For performance evaluation of preprocessing algorithms in IR small target images, SNR gain and BSF [12,[13][14][15][16]19] are widely accepted metrics. They are defined as:…”

Section: Description and Defect Analysis Of Traditional Metricsmentioning

confidence: 99%

“…Preprocessing is an indispensable stage, because it can reduce false-alarm rate and increase detection rate through suppressing background clutter and enhancing target signature. So far, a lot of preprocessing algorithms have been brought up, some focus on space domain and some care about frequency domain [1][2][3][4][5], such as two-dimensional least mean square (TDLMS) filter [6], morphological filter [7], high-pass filter [8], median filter [9], nonlinear filter [10,11], local variance weighted information entropy (WIE) filter [12]. It is well known that performance evaluation is an essential part for an effective algorithm, so we focus on evaluating the performance of preprocessing algorithms for IR small target images.…”

Section: Introductionmentioning

confidence: 99%

“…Signal-to-noise ratio (SNR) gain and background suppression factor (BSF) [12,[13][14][15][16] are the most popular metrics to evaluate preprocessing algorithms in IR small target images. Nevertheless, SNR could not accurately describe the quality of IR small target images and clutter standard deviation does not work well in denoting the complexity of background.…”

Section: Introductionmentioning

confidence: 99%

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Metrics for Performance Evaluation of Preprocessing Algorithms in Infrared Small Target Images

Diao¹,

Mao²,

Gui³

2011

PIER

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Abstract-Image preprocessing is commonly used in infrared (IR) small target detection to suppress background clutter and enhance target signature. To evaluate the performance of preprocessing algorithms, two performance metrics, namely PFTN (potential false targets number) decline ratio and BRI (background relative intensity) decline ratio are developed in this paper. The proposed metrics evaluate the performance of given preprocessing algorithm by comparing the qualities of input and output images. The new performance metrics are based on the theories of PFTN and BRI, which describe the quality of IR small target image, by representing the difficulty degree of target detection. Theoretical analysis and experimental results show that the proposed performance metrics can accurately reflect the effect of the image preprocessing stage on reducing false alarms and target shielding. Compared to the traditional metrics, such as signal-to-noise ratio gain and background suppression factor, the new ones are more intuitive and valid.

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“…To optimize this character, a derivative named the variance weighted information entropy (WIE) was applied firstly in the detection of infrared images. Due to the fact that infrared objects with different radiation usually appear to have distinct gray value in real images, the variance WIE has been proved to be a simple and effective quantitative description index for the complex degree of infrared image background [10]. Analogous to the infrared images, the SAR images are also nonuniform.…”

Section: Introductionmentioning

confidence: 99%

Fast target detection method for high-resolution SAR images based on variance weighted information entropy

Cao

Feng

2014

EURASIP J. Adv. Signal Process.

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Since the traditional CFAR algorithm is not suitable for high-resolution target detection of synthetic aperture radar (SAR) images, a new two-stage target detection method based on variance weighted information entropy is proposed in this paper. On the first stage, the regions of interest (ROIs) in SAR image is extracted based on the variance weighted information entropy (WIE), which has been proved to be a simple and effective quantitative description index for the complex degree of infrared image background. Considering that SAR images are nonuniform, an experiment is conducted ahead, in which the value of the variance WIE from a real SAR image in three areas with significant different uniform levels are tested and compared. The results preliminarily verified that the variance WIE is able to measure the complex degree of SAR images. After that, in order to make the segmentation efficient, the rough ROIs are further processed with a series of methods which adjust ROIs into regular pieces. On the second stage, for each of the ROIs, a variational segmentation algorithm based on the Split-Bregman algorithm is adopted to extract the target. In our experiment, the proposed method is tested on two kinds of SAR images, and its effectiveness is successfully demonstrated.

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Variance WIE based infrared images processing

Cited by 33 publications

References 1 publication

SAR image target detection in complex environments based on improved visual attention algorithm

SAR image target detection in complex environments based on improved visual attention algorithm

Metrics for Performance Evaluation of Preprocessing Algorithms in Infrared Small Target Images

Fast target detection method for high-resolution SAR images based on variance weighted information entropy

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