Adaptive Background Suppression Method Based on Intelligent Optimization for IR Small Target Detection Under Complex Cloud Backgrounds

Ren, Xiangyang; Wang, Jie; Ma, Tianlei; Yue, Caitong; Bai, K. Vijaya

doi:10.1109/access.2020.2974890

Cited by 5 publications

(3 citation statements)

References 51 publications

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“…[25]), thus our proposed algorithm can be considered as the pre-processing algorithm for the maritime target's detection and tracking. Moreover, in the filed of infrared target's segmentation [26,27] , this proposed algorithm can also improve the segmented target area's accuracy.…”

Section: Each Enhancement Algorithm's Results Of Infrared Maritime Imagementioning

confidence: 99%

Three Adaptive Sub-Histograms Equalization Algorithm for Maritime Image Enhancement

et al. 2020

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According to maritime image histograms' statistic and analysis, the histogram of pure maritime image obeys Gaussian distribution approximately, thus Three Adaptive Sub-histograms Equalization (TASHE) algorithm for maritime image enhancement is proposed in this paper. First, the characteristics of pure maritime image's histogram are studied, then the adaptive threshold's optimal selection strategy for the histogram's division is discussed, finally the implement of three sub-histograms is described. This paper employs visible gray maritime image, visible color maritime image and infrared maritime image to verify the enhancement algorithm's effectiveness and robustness, the experimental results show that TASHE algorithm can not only keep the maritime image's mean brightness and naturalness, but also improve the maritime image's contrast without the noise and artifacts. The objective image quality assessment also indicates that TASHE algorithm can improve the original maritime image's Enhancement Measure by Entropy (EME) value, furthermore, when a maritime image is pre-processed by TASHE algorithm, the maritime target's Detection Rate (DR) can be improved.

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Section: Each Enhancement Algorithm's Results Of Infrared Maritime Imagementioning

confidence: 99%

Three Adaptive Sub-Histograms Equalization Algorithm for Maritime Image Enhancement

et al. 2020

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“…In reference, 26 SCR of the reconstructed image is employed to assess the suitability of metrics for estimating average dielectric properties. In reference, 27 the background suppression performance is evaluated by SCR calculation to improve the detection ability of small targets in infrared image. In the proposed method, SCR is utilized as a metric in the simulated annealing.…”

Section: Methodsmentioning

confidence: 99%

Optimal microwave breast imaging using quality metrics and simulated annealing algorithm

Xiao

Liu

Song

et al. 2020

Int J RF Microw Comput Aided Eng

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Microwave breast imaging exploits dielectric contrasts between cancerous and healthy tissues at microwave frequencies to detect breast tumors. The estimation of breast optimal effective dielectric properties has a significant influence on the image quality. Recently, a novel method based on focal quality metrics (FQMs) has been proposed to investigate the patient‐specific effective dielectric properties. Although FQMs can be used to estimate the effective permittivity, the small increment of permittivity during the imaging process may lead to large computational resource consumption. In this article, an optimized microwave breast imaging method is proposed. In this method, a hybrid image quality metric is put forward which mixes the signal‐to‐clutter ratio (SCR) and focal quality metric. In addition, the simulated annealing algorithm is incorporated to facilitate image reconstruction. Experimental results demonstrate that using the proposed method, SCR is improved by 0.2 dB and tumor location error is reduced by 3.5 mm in realistic breast phantom.

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“…Infrared small target detection is a crucial technology to meet the growing demand for modern Omni-directional infrared search and track (IRST) system [1][2][3][4]. Owing to the long imaging distance between target and detector, the target is small in size and lacks structural priors in most cases [5][6][7].…”

Section: Introductionmentioning

confidence: 99%

Local contrast measure with iterative error for infrared small target detection

Yan

Xin

Zhang

2020

IET Image Processing

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Local contrast measure (LCM) has been proved to be an effective method for infrared small target detection. However, the detection performance of LCM decreases dramatically when the background contains strong edges and pixelsized noises with high brightness (PNHB). Based on the analysis of the inherent causes of the poor performance of LCM in extremely complex backgrounds, this study presents an effective LCM with an iterative error. The contribution is as follows: first, the two-dimensional least mean square (TDLMS) filter with an adaptive parameter is applied to suppress the background clutters roughly in each multiscale window. Then, the partial maximum pixel mean is applied to the LCM to optimise the subblock statistical parameters, which achieves excellent strong edges suppression performance. Finally, the iteration error generated by TDLMS and the sub-block weight matrix is updated alternately to further optimise the statistical parameters of the contrast measure to make it more effective in suppressing PNHB. Experimental results demonstrate that the proposed approach is not only superior to the contrast methods in terms of high detection efficiency and low false alarm rate but also has satisfactory adaptability under extremely complex backgrounds.

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Adaptive Background Suppression Method Based on Intelligent Optimization for IR Small Target Detection Under Complex Cloud Backgrounds

Cited by 5 publications

References 51 publications

Three Adaptive Sub-Histograms Equalization Algorithm for Maritime Image Enhancement

Three Adaptive Sub-Histograms Equalization Algorithm for Maritime Image Enhancement

Optimal microwave breast imaging using quality metrics and simulated annealing algorithm

Local contrast measure with iterative error for infrared small target detection

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