Infrared small target detection using tensor based least mean square

Liang, Hong; Wang, Qiang; Wang, Huan; Yang, Wankou

doi:10.1016/j.compeleceng.2021.106994

Cited by 12 publications

(12 citation statements)

References 9 publications

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“…We select MDvsFAcGAN [27], ALCNet [34], ACMNet [23], and IAANet [36], UIUNet [35], and CGRANet [37] as the CNN-based methods. To compare the traditional methods, we select the fliter-based method SRTHT [11], Maxmean/median based method TLMS [12], the HVS-based method MPCM [14], and the LRM-based method: IPI [17], RIPT [16]. The key parameters configuration are shown in Tab.…”

Section: Experiments Resultsmentioning

confidence: 99%

“…Filter-based methods focus on suppressing backgrounds clutter, For instance, Bai et al [9] proposed the Top-Hat filter as a filtering-based method. Li et al [12] utilize both maxmean/max-median filter to detection small targets. However, this type of methods have some limitations, such as the model parameters enable to automatically adjusted on complex backgrounds, making it suitable only for single backgrounds and uniform scenes.…”

Section: Related Work a Single Infrared Small Target Detectionmentioning

confidence: 99%

“…Traditional SISTD methods can be classified into three classes, including filter-based, human vision system(HVS)based, and low-rank matrix(LRM)-based methods. Filter-based methods [9]- [12] employ specifically designed filters to extract small target from the backgrounds. While they are effective in filtering out smooth backgrounds clutter, their performance degrades significantly when encountering noise and background interference of varying intensity.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Toward Accurate Infrared Small Target Detection via Edge-Aware Gated Transformer

Zhu,

Ma,

Fan

et al. 2024

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

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Extracting small targets from complex backgrounds is the eventual goal of single infrared small target detection (SISTD), which has many potential applications in defense security and marine rescue. Recently, methods utilizing deep learning have shown their superiority over traditional theoretical approaches. However, they do not consider both the global semantics and specific shape information, thereby limiting their performance. To overcome this proplem, we propose a Gated Shaped TransUnet (GSTUnet), designed to fully utilize shape information while detecting small target detection. Specifically, we have proposed a multi-scale encoder branch to extract global features of small targets at different scales. Then, the extracted global features are passed through a gated-shaped stream branch that focuses on the shape information of small targets through gate convolutions. Finally, we fuse there features to obtain the final result. Our GSTUnet learns both global and shape information through the aforementioned two branches, establishing global relationships between different feature scales. The GSTUnet demonstrates excellent evaluation metrics on various datasets, outperforming current state-of-the-art methods. To access our code and datasets, please visit the following link: https://github.com/

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Section: Experiments Resultsmentioning

confidence: 99%

Section: Related Work a Single Infrared Small Target Detectionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Toward Accurate Infrared Small Target Detection via Edge-Aware Gated Transformer

Zhu,

Ma,

Fan

et al. 2024

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

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show abstract

“…Based on any point on a developable surface is located in its neighborhood within a certain point on the plane of the prior conditions, in this article the tangent plane is based on eight areas, with a triangular configuration tangent plane projection operator is a total of 12, as shown in Figure 2, the black circle is a center pixel, white circle represents the adjacent pixels, and dotted line is the neighborhood of tangent plane 14 . In fact, there are only four types from the center point to the eight tangent planes.…”

Section: Gaussian Curvature Filteringmentioning

confidence: 99%

Infrared small target detection based on Gaussian curvature filtering and partial sum of singular values

Xie

Liu

2022

Third International Conference on Computer Science and Communication Technology (ICCSCT 2022)

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To reduce noise and background distractions, we propose a detection algorithm based of Gaussian curvature filtering (GCF) and partial sum of singular values (PSSV). Above all, aiming at the false alarms caused by noise, using the prior knowledge of natural image have the characteristics of approximate developable, GCF utilizes the variation model to obtain an approximately noise-free image. Secondly, we adopt PSSV to suppress the background by pointing out that the reason why Robust PCA is inaccurate in the background estimation is that the hypothesis at the edge of the background does not match the reality, and we present a model solving algorithm on the ground of imprecise augmented Lagrangian multiplier method (IALM). At last, we use an adaptive threshold segmentation algorithm to segment the target. The model has better noise clutter suppression and detection accuracy than other representative methods.

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“…Traditional detection algorithms include bilateral filtering [6][7], anisotropic filtering [8][9][10][11], TDLMS filtering VOLUME XX, 2021 [12][13][14], gradient inverse filtering [15][16][17], Markov random field model [18][19][20][21][22][23] etc. These algorithms that preserve the target signal while processing the image background have achieved relatively good results.…”

Section: Introductionmentioning

confidence: 99%

Dim and Small Target Detection Based on Gaussian Markov Random Field Motion Direction Estimation

Min

Fan

et al. 2022

IEEE Access

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To improve the detection of dim and small targets in low signal-to-noise ratio (SNR<3dB) scenarios, energy accumulation along the estimated direction by estimating the direction of the target's motion at different moments is a proven approach, and considering that the Gaussian Markov random field can well describe the correlation of image element points in their air and time domains, Gaussian Markov random field is introduced into the paper to process the image. Firstly, by combining the proximity of image element spatial coordinates and the correlation of image element values to construct a new adaptive Gaussian weighted Markov random field filtering model, which fully incorporates the spatio-temporal information of image element coordinates and the correlation degree information of image element values for weighting to describe the image element neighborhood system. Second, to further enhance the energy of weak targets, the energy accumulation of targets is achieved by establishing a Gaussian Markov random field motion direction estimation model on the basis of obtaining filtered images. Through experiments, it is shown that the algorithm proposed in this paper can effectively accumulate the weak signal of the target between frames, and the target signal-to-noise ratio can reach more than 6dB compared with that before enhancement, effectively improving the detection ability of weak targets.INDEX TERMS Dim and small targets; detection; Gaussian Markov random field; energy accumulation; motion estimation

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Infrared small target detection using tensor based least mean square

Cited by 12 publications

References 9 publications

Toward Accurate Infrared Small Target Detection via Edge-Aware Gated Transformer

Toward Accurate Infrared Small Target Detection via Edge-Aware Gated Transformer

Infrared small target detection based on Gaussian curvature filtering and partial sum of singular values

Dim and Small Target Detection Based on Gaussian Markov Random Field Motion Direction Estimation

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