Infrared small target detection based on an image-patch tensor model

Zhang, Xiangyue; Ding, Qinghai; Luo, Haibo; Bu, Hui; Chang, Zheng; Zhang, Junchao

doi:10.1016/j.infrared.2019.03.009

Cited by 33 publications

(14 citation statements)

References 29 publications

(43 reference statements)

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“…A local and global priors Reweighted Infrared Patch Tensor (RIPT) model was described in [9]. Zhang et al [10] modified IPI by introducing a three dimension tensor model. Xue et al [11] introduced multiple sparse constraints in reconstruction.…”

Section: ) Background Large Spatial Spread Characteristic Based Methodsmentioning

confidence: 99%

Small Aerial Target Detection for Airborne Infrared Detection Systems Using LightGBM and Trajectory Constraints

Sun

Guo

Zhang

et al. 2021

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

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Factors, such as rapid relative motion, clutter background, etc., make robust small aerial target detection for airborne infrared detection systems a challenge. Existing methods are facing difficulties when dealing with such cases. We consider that a continuous and smooth trajectory is critical in boosting small infrared aerial target detection performance. A simple and effective small aerial target detection method for airborne infrared detection system using LightGBM and trajectory constraints is proposed in this paper. First, we simply formulate target candidate detection as a binary classification problem. Target candidates in every individual frame are detected via interesting pixel detection and a trained LightGBM model. Then, the local smoothness and global continuous characteristic of the target trajectory are modeled as short-strict and long-loose constraints. The trajectory constraints are used efficiently for detecting the true small infrared aerial targets from numerous target candidates. Experiments on public datasets demonstrate that the proposed method performs better than other existing methods. Furthermore, a public dataset for small aerial target detection in airborne infrared detection systems is constructed (https://small-infrared-aerial-target-detection.grand-challenge.or g/). To the best of our knowledge, this dataset has the largest data scale and richest scene types within this field.

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Section: ) Background Large Spatial Spread Characteristic Based Methodsmentioning

confidence: 99%

Small Aerial Target Detection for Airborne Infrared Detection Systems Using LightGBM and Trajectory Constraints

Sun

Guo

Zhang

et al. 2021

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

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show abstract

“…In recent years, the singular value decomposition of matrix has been extended to tensor space and the tensor singular value decomposition (t-SVD) was proposed [46]. The tensor nuclear norm (TNN) derived from t-SVD has been proposed and extensively studied and applied [46][47][48][49][50]. One disadvantage of TNN is that all singular values are treated equally.…”

Section: Motivationmentioning

confidence: 99%

Infrared Small Target Detection via Non-Convex Tensor Rank Surrogate Joint Local Contrast Energy

et al. 2020

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Small target detection is a crucial technique that restricts the performance of many infrared imaging systems. In this paper, a novel detection model of infrared small target via non-convex tensor rank surrogate joint local contrast energy (NTRS) is proposed. To improve the latest infrared patch-tensor (IPT) model, a non-convex tensor rank surrogate merging tensor nuclear norm (TNN) and the Laplace function, is utilized for low rank background patch-tensor constraint, which has a useful property of adaptively allocating weight for every singular value and can better approximate l 0 -norm. Considering that the local prior map can be equivalent to the saliency map, we introduce a local contrast energy feature into IPT detection framework to weight target tensor, which can efficiently suppress the background and preserve the target simultaneously. Besides, to remove the structured edges more thoroughly, we suggest an additional structured sparse regularization term using the l 1 , 1 , 2 -norm of third-order tensor. To solve the proposed model, a high-efficiency optimization way based on alternating direction method of multipliers with the fast computing of tensor singular value decomposition is designed. Finally, an adaptive threshold is utilized to extract real targets of the reconstructed target image. A series of experimental results show that the proposed method has robust detection performance and outperforms the other advanced methods.

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“…Image edge detection algorithm is of great significance to the research of image processing and plays an important role in image segmentation [28], image mosaic [29] and image target detection [30]. In recent years, classic image edge extraction algorithms such as Prewitt [31], Kirsch [32], Sobel [33], and Canny [34] have been continuously proposed.…”

Section: Introductionmentioning

confidence: 99%

Demonstration of Quantum Image Edge Extration Enhancement Through Improved Sobel Operator

Chu

2020

IEEE Access

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Quantum image processing is a system where both quantum computing and image processings are key. Now it is required to investigate how to apply image processing concepts such as image edge detection and improved Sobel operator, to quantum image processing. Just as traditional image processing helps to image manipulation, our efforts in quantum image processing contribute to the further development of quantum algorithm and quantum information theory. Edge detection is an important problem in traditional image processing, and image edge detection algorithm can filter image edge features and retain important attributes. We proposed a quantum edge detection algorithm, which employs improved Sobel operator to improve the performance, so as to solve the problem of unsatisfactory traditional image edge detection methods. In practical application, the amount of image data to be processed increases sharply, and the computing power of classical computer becomes a limitation. Quantum information processing can effectively accelerate many classical problems by virtue of quantum mechanical characteristics, such as quantum superposition, entanglement, parallelism. Our proposed method uses the novel enhanced quantum representation to store quantum images, which stores all the pixels in the image in a superimposed state, realizing parallel computation. The improved eight-direction Sobel operator is used to calculate the gray gradient, and the quantum circuit is designed to realize quantum edge detection. Simulation results have shown that our algorithm can extract edge with dimension 2 n × 2 n when the computational complexity is O(n 2 + 2 q+3). The improved algorithm can detect more edge details and has strong adaptability.

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Infrared small target detection based on an image-patch tensor model

Cited by 33 publications

References 29 publications

Small Aerial Target Detection for Airborne Infrared Detection Systems Using LightGBM and Trajectory Constraints

Small Aerial Target Detection for Airborne Infrared Detection Systems Using LightGBM and Trajectory Constraints

Infrared Small Target Detection via Non-Convex Tensor Rank Surrogate Joint Local Contrast Energy

Demonstration of Quantum Image Edge Extration Enhancement Through Improved Sobel Operator

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