Infrared Dim and Small Target Detection Based on Greedy Bilateral Factorization in Image Sequences

Pang, Dongdong; Shan, Tao; Li, Wei; Ma, Ping; Liu, Shengheng; Tao, Ran

doi:10.1109/jstars.2020.2998822

Cited by 40 publications

(17 citation statements)

References 39 publications

(52 reference statements)

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“…Kong [21] expands the tensor rank to tensor fiber rank by multimodal t-SVD, while using hyper-total variation (HTV) as a regularization term to achieve constraints on the background and retention of target information. Pang [22] proposed a low-rank and sparse decomposition method based on greedy bilateral decomposition, which analyzes the information of the motion target in the sequence image and treats the target as the sparse part, while the low-rank part is approximated by using the bilateral factorization strategy to greatly improve the operation efficiency of the algorithm. Wang [23] introduced isotropic all-variance theory on the basis of IPI model, which makes full use of the local autocorrelation of the background image through IPI model and achieves joint regularization on the basis of the original principal component tracking model with the help of isotropic all-variance, which well preserves the background information of the image and thus establishes a good background model, but the all-variance theory used in this algorithm only relies on the null domain local correlation of the image block for background constraint, and there is insufficient information utilization, and when the background is in dynamic change (e.g., illumination change, non-smooth background), there are some edge contour false target points in the difference image.…”

Section: Background Forecastmentioning

confidence: 99%

Infrared Dim and Small Target Detection Algorithm Combining Multiway Gradient Regularized Principal Component Decomposition Model

Fan

Chen

et al. 2022

IEEE Access

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In complex non-smooth backgrounds, infrared dim and small target targets generally have lower energy and occupy fewer pixels, and are easily swamped by clutter. To improve the detection capability of dim and small targets in non-smooth scenes, this paper proposes a new dim and small target detection method combining multidirectional gradient difference regularization principal component decomposition model. The method first establishes a new gradient difference regularization to constrain the low-rank subspaces of different image components, then construct a gradient difference regularization-based principal component decomposition model (GDR-PCD), and finally decomposes the model using the overlapping directional multiplier method to obtain the background impedance. The experimental results show that the method performs better in all six sequential image scenes than the traditional algorithm. Furthermore, the detection results verify the algorithm's effectiveness in this paper.INDEX TERMS infrared dim and small target detection, principal component decomposition model, gradient difference regularization principal component decomposition model, overlapping directional multiplier method

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Section: Background Forecastmentioning

confidence: 99%

Infrared Dim and Small Target Detection Algorithm Combining Multiway Gradient Regularized Principal Component Decomposition Model

Fan

Chen

et al. 2022

IEEE Access

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“…In recent decades, researchers have proposed a large number of small target detection methods for infrared search and track systems. Roughly speaking, they can be simply categorized into two groups: single frame-based methods [5] [7] [10] [13] and multi-frame-based methods [4] [9] [11] [24] [25]. Given prior knowledge of target velocity and trajectory, multiframe-based methods process a sequence of frames to detect targets.…”

Section: Related Workmentioning

confidence: 99%

IISTD: Image Inpainting-Based Small Target Detection in a Single Infrared Image

Ling

Zhang

et al. 2022

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

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Small target detection is a crucial and challenging task in infrared search and track system. Background estimationbased methods is an effective and important approach for infrared small target detection. Affected by the target pixels, existing background estimation methods may reconstruct an inaccurate background. Based on image inpainting technique, we propose a novel two-stage approach to predict more accurate backgrounds. At the first stage, the inner and outer windowbased image inpainting (IOWII) is used to obtain a rough background estimation. Then a mask of candidate target region is automatically obtained by calculating and evaluating the difference between raw image and rough background. In the second stage, the final accurate background is predicted by maskbased image inpainting (MII). It recovers the removed candidate target area using the information of surrounding background pixels, avoiding target pixels to participate in the calculation of background reconstruction. Finally, the target saliency map is obtained by subtracting the final estimated background from the original image, and a simple adaptive threshold is used to segment the target. Experimental results on real infrared images and sequences demonstrate that the proposed method outperforms other state-of-the-arts. It is simple and effective, with strong robustness and good real-time performance.

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“…It includes anisotropic filtering [8][9], top hat filtering [3,10,11], Max mean filtering [12], spatiotemporal significance model [13] and greedy bilateral factorization model [14], as well as detection methods constructed according to the low rank characteristics of image background, such as IPI model [15], RPCA model [16,17], MPCM model [18], TV-PCP model [18], LCM model [20], DPA model [21], CDAE model [22] and other algorithms have contributed to the detection of dim and small targets. For example, the spatiotemporal saliency model [13] and the greedy bilateral factorization model [14] proposed by Pang et al Literature [13] fully fuse the time domain information and spatial domain information of the image to complete the background modeling of the image, and obtain the saliency region in the image, so as to retain and enhance the target signal. Finally, using the motion characteristics of target to detect and extract real target through adaptive threshold segmentation, and the effect of background modeling in low altitude and long-distance scene is better.…”

Section: Introductionmentioning

confidence: 99%

Dim and Small Target Detection Based on Spiral Gradient Optimization Estimation and High-Order Correlation Enhancement

Fan

Min

et al. 2022

IEEE Access

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In order to reduce the influence of strong halo effect on dim small target detection in daytime, a dim small target detection algorithm based on spiral gradient optimization estimation and high-order correlation enhancement is proposed in this paper. In this paper, we first design a spiral motion model to obtain the local gradient information in the central image point by perturbing the designed motion direction, then estimate the optimal background by establishing a gradient optimization model to achieve background suppression while effectively removing the halo phenomenon. Considering the original high-order correlation model only uses a single pixel for motion correlation, there is insufficient information utilization, an improved high-order correlation energy enhancement model is proposed to enhance the target signal, the algorithm first constructs an attention discrimination model based on inner and outer windows to obtain the salient region of the image, and then carries out multi frame high-order motion correlation of neighborhood blocks to enhance the target energy. After experiments, it is shown that compared with the traditional algorithm, the algorithm proposed in this paper can effectively weaken the halo effect while suppressing most of the background and can effectively enhance the local signal-to-noise ratio of the target.INDEX TERMS Dim and small target detection; Background suppression; Spiral gradient; High order correlation; Energy enhancement.

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Infrared Dim and Small Target Detection Based on Greedy Bilateral Factorization in Image Sequences

Cited by 40 publications

References 39 publications

Infrared Dim and Small Target Detection Algorithm Combining Multiway Gradient Regularized Principal Component Decomposition Model

Infrared Dim and Small Target Detection Algorithm Combining Multiway Gradient Regularized Principal Component Decomposition Model

IISTD: Image Inpainting-Based Small Target Detection in a Single Infrared Image

Dim and Small Target Detection Based on Spiral Gradient Optimization Estimation and High-Order Correlation Enhancement

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