Infrared Small Target Detection Based on Weighted Three-Layer Window Local Contrast

Cui, Huixin; Li, Liyuan; Liu, Xin; Su, Xin; Chen, Fansheng

doi:10.1109/lgrs.2021.3133649

Cited by 26 publications

(15 citation statements)

References 13 publications

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“…The figures display the intuitive effect of all the algorithms. The detection comparison of the Tophat, max-mean filter, max-median filter, MPCM [12], LMLCM [20], WSLCM [21], and our method is shown in Figures 8-13 All the methods were compared for the complex background image sequences (1), (2), and (3) captured in the outfield. The SCRG and BSF of image (1) are shown in Table 4.…”

Section: Simulation Results and Comparisonmentioning

confidence: 99%

An Adaptive Infrared Small-Target-Detection Fusion Algorithm Based on Multiscale Local Gradient Contrast for Remote Sensing

et al. 2023

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Space vehicles such as missiles and aircraft have relatively long tracking distances. Infrared (IR) detectors are used for small target detection. The target presents point target characteristics, which lack contour, shape, and texture information. The high-brightness cloud edge and high noise have an impact on the detection of small targets because of the complex background of the sky and ground environment. Traditional template-based filtering and local contrast-based methods do not distinguish between different complex background environments, and their strategy is to unify small-target template detection or to use absolute contrast differences; so, it is easy to have a high false alarm rate. It is necessary to study the detection and tracking methods in complex backgrounds and low signal-to-clutter ratios (SCRs). We use the complexity difference as a prior condition for detection in the background of thick clouds and ground highlight buildings. Then, we use the spatial domain filtering and improved local contrast joint algorithm to obtain a significant area. We also provide a new definition of gradient uniformity through the improvement of the local gradient method, which could further enhance the target contrast. It is important to distinguish between small targets, highlighted background edges, and noise. Furthermore, the method can be used for parallel computing. Compared with the traditional space filtering algorithm or local contrast algorithm, the flexible fusion strategy can achieve the rapid detection of small targets with a higher signal-to-clutter ratio gain (SCRG) and background suppression factor (BSF).

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Section: Simulation Results and Comparisonmentioning

confidence: 99%

An Adaptive Infrared Small-Target-Detection Fusion Algorithm Based on Multiscale Local Gradient Contrast for Remote Sensing

et al. 2023

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“…In this paper, three performance evaluation metrics, Signal to Clutter Ratio Gain (SCRG), Background Suppression Factor (BSF), and Receiver Operating Characteristic (ROC) curve, are selected to quantitatively evaluate the performance of small-target detection methods [65], [66].…”

Section: A Measurement Metrics and Comparison Methodsmentioning

confidence: 99%

“…SCRG [65] is employed to analyze the target enhancement performance of the method. The higher the SNRG, the better the enhancement effect of the target.…”

Section: A Measurement Metrics and Comparison Methodsmentioning

confidence: 99%

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A Compact Mid-Wave Infrared Imager System With Real-Time Target Detection and Tracking

Deng

Wang

et al. 2022

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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“…Finally, the algorithm identifies the suspected target area. There have also been many improved algorithms based on LCM in recent years, such as Multi-Scale Patch-Based Contrast Measure (MPCM) [26], Weighted Three-Layer Window Local-Contrast Method (WTLLCM) [27], Integrated Target Saliency Measure (ITSM) [28], Relative Local Contrast Measure (RLCM) [29], Multi-Directional Gradient Improved Double-Layer Local-Contrast Method (MGIDLCM) [30], and Maritime Noise Prior (MNP) [31]. The main principle of the MPCM is to calculate the grayscale mean difference in eight different directions of surrounding patches, which can generate the dissimilarity measure between surrounding patches and form a multi-scale patch-based contrast measure.…”

Section: Introductionmentioning

confidence: 99%

An Effective Method of Infrared Maritime Target Enhancement and Detection with Multiple Maritime Scene

et al. 2023

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Aiming at maritime infrared target detection with low contrast influenced by maritime clutter and illumination, this paper proposes a Modified Histogram Equalization with Edge Fusion (MHEEF) pre-processing algorithm in backlight maritime scenes and establishes Local-Contrast Saliency Models with Double Scale and Modes (LCMDSM) for detecting a target with the properties of positive and negative contrast. We propose a local-contrast saliency mathematical model with double modes in the extension of only one mode. Then, the big scale and small scale are combined into one Target Detection Unit (TDU), which can approach the “from bottom to up” mechanism of the Visual Attention Model (VAM) better and identify the target with a suitable size, approaching the target’s actual shape. In the experimental results and analysis, clutter, foggy, backlight, and dim maritime scenes are chosen to verify the effectiveness of the target detection algorithm. From the enhancement result, the LCMDSM algorithm can achieve a Detection Rate (DR) with a value of 98.26% under each maritime scene on the average level and can be used in real-time detection with low computational cost.

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Infrared Small Target Detection Based on Weighted Three-Layer Window Local Contrast

Cited by 26 publications

References 13 publications

An Adaptive Infrared Small-Target-Detection Fusion Algorithm Based on Multiscale Local Gradient Contrast for Remote Sensing

An Adaptive Infrared Small-Target-Detection Fusion Algorithm Based on Multiscale Local Gradient Contrast for Remote Sensing

A Compact Mid-Wave Infrared Imager System With Real-Time Target Detection and Tracking

An Effective Method of Infrared Maritime Target Enhancement and Detection with Multiple Maritime Scene

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