Point target detection based on multiscale morphological filtering and an energy concentration criterion

Liu, Rang; Wang, Dejiang; Zhou, Dabiao; Jia, Ping

doi:10.1364/ao.56.006796

Cited by 9 publications

(5 citation statements)

References 16 publications

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“…This kind of method based on the assumption of the infrared image is that the target is isolated from a relatively continuous background and detection of the target via the suppression of the background. Then max-mean\max-median method [8] and the morphology opening method [9] are proposed to remove the background. Although these methods have the advantage of a low complexity calculation, the estimation accuracy will be greatly affected under the complex background.…”

Section: The Background Suppression-based Methodsmentioning

confidence: 99%

Infrared Dim Small Target Detection Based on Nonconvex Constraint with L1–L2 Norm and Total Variation

Shi

et al. 2023

Remote Sensing

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Infrared dim small target detection has received a lot of attention, because it is a crucial component of the IR search and track systems (IRST). The robust principal component analysis (RPCA) is a common detection framework, which works with poor performance with complex background edges and sparse clutters due to the inappropriate approximation of sparse items. A nonconvex constraint detection method based on the difference between the L1 and L2 (L1–L2) norm and total variation (TV) is presented. The L1–L2 norm is a more accurate sparse item approximation of L0 norm, which can achieve a better description of the sparse item to separate the target from the complex backgrounds. Then, the total variation norm is conducted on the target image to suppress the sparse clutters. The new model is solved using the alternating direction method of multipliers (ADMM) method. Then, the subproblems in the model are tackled by the difference of convex algorithm (DCA) and the Newton conjugate gradient (Newton-CG) solving L1–L2 norm and TV norm, respectively. In the experiment, we conducted experiments on multiple and single target datasets, and the proposed model outperforms the state-of-the-art (SOTA) methods in terms of background suppression and robustness to accurately detect the target. It can achieve a higher true position rate (TPR) with a low false position rate (FPR).

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Section: The Background Suppression-based Methodsmentioning

confidence: 99%

Infrared Dim Small Target Detection Based on Nonconvex Constraint with L1–L2 Norm and Total Variation

Shi

et al. 2023

Remote Sensing

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“…Due to sensor hardware limitations, atmospheric interference, and the influence of optical systems, as the Figure 1(b), the grayscale of spatial point targets gradually decreases from the center to the surrounding areas. The imaging of point targets can be approximated by point spread function (PSF) [29]:…”

Section: Imaging Characteristicsmentioning

confidence: 99%

Non-Uniform Background Noise Suppression Method Based on Improved Total Variation Model for Wide-Field Imaging System

Zhou,

Wu,

Wang

et al. 2024

IEEE Access

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A wide-field surveillance system with a long exposure time has a stronger detectability for dim space targets. However, with the increase in exposure time and working temperature, complex non-uniform background noise containing hot pixels of the detector cannot be ignored, seriously affecting the background and imaging quality. This article studies and proposes a high-performance denoising method, which does not use any prior knowledge of the target and can automatically remove noise from the image. This method is based on an improved total variation model to remove hot pixels and other background mixed noise in widefield system images. Firstly, using the idea of the traditional local contrast method (LCM), we utilize the significant difference in grayscale values between contaminated pixels and neighboring pixels to detect impulse noise, such as the hot pixels in the image. And then, we designed an improved adaptive maximum filtering algorithm to remove unwanted contamination, which protected target information from being lost and optimized pixels that were attacked by impulse noise. Finally, the total variation algorithm is used to eliminate residual background noise, the detector's readout noise, and non-uniform response. The method proposed in this article can effectively filter out hot pixels and non-uniform background noise while preserving the details of target edges. We conducted experiments on a large number of simulated and original images. For star maps captured in long exposure mode, the method proposed in this article has obvious advantages over several competing algorithms. The experimental results show that, compared to competitive algorithms, the algorithm proposed in this article improves PSNR by at least 13.1%, SSIM by at least 0.4%, IEF by at least 5 times, and IQI by at least 9.2%. At the same time, the algorithm in this article achieved a moderate level of computation time.INDEX TERMS wide-field surveillance system; long exposure time; non-uniform correction; local contrast method; maximum filter; total variation

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“…where b n represents different structural elements, and TH n represents the resulting image of the Top-hat transform by b n . We introduce the eight omnidirectional multiscale structural elements with the size of 5 × 5 dimensions which designed in our previous work [3] direction, and 8 b of 315° direction. "1" and "0" are the basic binary morphological operators.…”

Section: Omnidirectional Multiscale Morphological Filteringmentioning

confidence: 99%

“…Dim point target detection under complex background is a key technology in numerous fields, including infrared search and track (IRST) systems, terminal guidance, external intrusion warnings, and medical monitoring [1][2][3]. When the aerial target is far away from the infrared focal plane array (IRFPA), the signal intensity is very weak, and the minutiae are very small.…”

Section: Introductionmentioning

confidence: 99%

“…Bai et al [36,37] presented a multiscale center-surround Top-hat transform through constructing two structural elements and successfully extracted regions of interest (RoIs) which were richer in image details than using single structural element. In our previous research [3], multiscale morphological filtering combining Top-hat and Bottom-hat is proposed to detect all the possible targets, and the energy concentration criterion is adopted to eliminate false alarms, which perform a better background and noise suppression under single-band model. For the dual-band model, multiscale Top-hat transform could be optimized by the omnidirectional structural element to achieve better background suppression.…”

Section: Introductionmentioning

confidence: 99%

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An Omnidirectional Morphological Method for Aerial Point Target Detection Based on Infrared Dual-Band Model

et al. 2018

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Aerial infrared point target detection under nonstationary background clutter is a crucial yet challenging issue in the field of remote sensing. This paper presents a novel omnidirectional multiscale morphological method for aerial point target detection based on a dual-band model. Considering that the clutter noise conforms to the Gaussian distribution, the single-band detection model under the Neyman-Pearson (NP) criterion is established first, and then the optimal fused probability of detection under the dual-band model is deduced according to the And fusion rule. Next, the omnidirectional multiscale morphological Top-hat algorithm is proposed to extract all the possible targets distributing in every direction, and the local difference criterion is employed to eliminate the residual background edges further. The dynamic threshold-to-noise ratio (TNR) is adjusted to obtain the optimal probability of detection under the constant false alarm rate (CFAR) criterion. Finally, the dim point target is extracted after dual-band data correlation. The experimental result demonstrates that the proposed method achieves a high probability of detection and performs well with respect to suppressing complex background when compared with common algorithms. In addition, it also has the advantage of low complexity and easy implementation in real-time systems.

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Point target detection based on multiscale morphological filtering and an energy concentration criterion

Cited by 9 publications

References 16 publications

Infrared Dim Small Target Detection Based on Nonconvex Constraint with L1–L2 Norm and Total Variation

Infrared Dim Small Target Detection Based on Nonconvex Constraint with L1–L2 Norm and Total Variation

Non-Uniform Background Noise Suppression Method Based on Improved Total Variation Model for Wide-Field Imaging System

An Omnidirectional Morphological Method for Aerial Point Target Detection Based on Infrared Dual-Band Model

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