Infrared Small Target Detection Based on the Weighted Double Local Contrast Measure Utilizing a Novel Window

Lu, Xiaofeng; Bai, Xiaofei; Li, Sixun; Hei, Xinhong

doi:10.1109/lgrs.2022.3194602

Cited by 13 publications

(3 citation statements)

References 14 publications

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“…The local contrast measure (LCM) [16] provides a basic target enhancement model. Various improved HVS algorithms [17][18][19][20][21][22][23][24] have been proposed, which usually involve weighted enhancement functions [19] or multi-layer filtering windows [20] or incorporate preprocessing operations [22] to increase detection accuracy, leading to a more intricate detector structure. Motivated by the temporal domain correlation, studies [25][26][27][28] have combined the spatial and temporal contrast features and proposed a spatial-temporal local contrast filter (STLCF) [25], interframe registration, and spatial local contrast (IFR-SLC) [27] and spatial-temporal local difference measure (STLDM) [28].…”

Section: The Deep Learning Methodsmentioning

confidence: 99%

“…Single-frame Human visual system-based LCM [16], Improved LCM (ILCM) [17], Relative LCM (RLCM) [18], Weighted LCM (WLDM) [19], Weighted Double LCM (WDLCM) [20], Weighted Local Ratio-Difference Contrast Method (WLRDCM) [21], Neighborhood Saliency Map (NSM) [22], multi-scale Tri-Layer LCM (TLLCM) [23], and Weighted Strengthened LCM (WSLCM) [24].…”

Section: Image Filtering-basedmentioning

confidence: 99%

See 1 more Smart Citation

A Space Target Detection Method Based on Spatial–Temporal Local Registration in Complicated Backgrounds

Su,

Chen,

Cang

et al. 2024

Remote Sensing

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Human space exploration has brought a growing crowded operating environment for in-orbit spacecraft. Monitoring the space environment and detecting space targets with photoelectric equipment has extensive and realistic significance in space safety. In this study, a local spatial–temporal registration (LSTR) method is proposed to detect moving small targets in space. Firstly, we applied the local region registration to estimate the neighbor background motion model. Secondly, we analyzed the temporal local grayscale difference between the strong clutter and target region and measured the temporal local–central region difference to enhance the target. Then, the temporal pixel contrast map was calculated, which further retains the target signal and suppresses the residue clutter. Finally, a simple adaptive threshold segmentation algorithm was applied to the saliency map to segment the targets. Comparative experiments were conducted on four groups of image sequences to validate the efficiency and robustness of the algorithm. The experimental findings indicate that the proposed method performs well in target enhancement and clutter suppression under different scenarios.

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Section: The Deep Learning Methodsmentioning

confidence: 99%

Section: Image Filtering-basedmentioning

confidence: 99%

A Space Target Detection Method Based on Spatial–Temporal Local Registration in Complicated Backgrounds

Su,

Chen,

Cang

et al. 2024

Remote Sensing

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“…Based on the human visual system, [20][21][22] incorporate mathematical functions that match the characteristics of small infrared targets. Weighted double local contrast measure (WDLCM) [23] utilizes a weighted function to fuse the standard deviation between the target area and the background area, as well as the variance within the target area, to enhance the target. However, the HVS method may not be effective in detecting small targets with lower brightness than the background.…”

Section: Introductionmentioning

confidence: 99%

Residual Depth Feature-Extraction Network for Infrared Small-Target Detection

Lizhe

Zhang

et al. 2023

Electronics

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Deep-learning methods have exhibited exceptional performance in numerous target-detection domains, and their application is steadily expanding to include infrared small-target detection as well. However, the effect of existing deep-learning methods is weakened due to the lack of texture information and the low signal-to-noise ratio of infrared small-target images. To detect small targets in infrared images with limited information, a depth feature-extraction network based on a residual module is proposed in this paper. First, a global attention guidance enhancement module (GAGEM) is used to enhance the original infrared small target image in a single frame, which considers the global and local features. Second, this paper proposes a depth feature-extraction module (DFEM) for depth feature extraction. Our IRST-Involution adds the attention mechanism to the classic Involution module and combines it with the residual module for the feature extraction of the backbone network. Finally, the feature pyramid with self-learning weight parameters is used for feature fusion. The comparative experiments on three public datasets demonstrate that our proposed infrared small-target detection algorithm exhibits higher detection accuracy and better robustness.

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