Infrared Small Target Detection under Complex Background

Peng, Gui Hua; Chen, He; Wu, Qiang

doi:10.4028/www.scientific.net/amr.346.615

Cited by 5 publications

(4 citation statements)

References 3 publications

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“…Table 2 shows the denoising PSNR and SSIM of image (1) with different noise std. Table 3 shows the denoising PSNR and SSIM of image (2) with different noise std.…”

Section: Resultsmentioning

confidence: 99%

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A Novel Adaptive Group Sparse Representation Model Based on Infrared Image Denoising for Remote Sensing Application

Chen

Zhu

et al. 2023

Applied Sciences

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Infrared (IR) Image preprocessing is aimed at image denoising and enhancement to help with small target detection. According to the sparse representation theory, the IR original image is low rank, and the coefficient shows a sparse character. The low rank and sparse model could distinguish between the original image and noise. The IR images lack texture and details. In IR images, the small target is hard to recognize. Traditional denoising methods based on nuclear norm minimization (NNM) treat all eigenvalues equally, which blurs the concrete details. They are unable to achieve a good denoising performance. Deep learning methods necessitate a large number of train images, which are difficult to obtain in IR image denoising. It is difficult to perform well under high noise in IR image denoising. Tracking and detection would not be possible without a proper denoising method. This article fuses the weighted nuclear norm minimization (WNNM) with an adaptive similar patch, searching based on the group sparse representation for infrared images. We adaptively selected similar structural blocks based on certain computational criteria, and we used the K-nearest neighbor (KNN) cluster to constitute more similar groups, which is helpful in recovering the complex background with high Gaussian noise. Then, we shrank all eigenvalues with different weights in the WNNM model to solve the optimization problem. Our method could recover more detailed information in the images. The algorithm not only obtains good denoising results in common image denoising but also achieves good performance in infrared image denoising. The target in IR images attains a high signal for the clutter in IR detection systems for remote sensing. Under common data sets and real infrared images, it has a good noise suppression effect with a high peak signal-to-noise ratio (PSNR) and structural similarity index measurement (SSIM), with higher noise and a much more complex background.

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“…Table 2 shows the denoising PSNR and SSIM of image (1) with different noise std. Table 3 shows the denoising PSNR and SSIM of image (2) with different noise std.…”

Section: Resultsmentioning

confidence: 99%

“…The uncooled IR focal plane array imaging technology has some advantages such as lower weight and power consumption. It is widely used in IR detectors [1]. Meanwhile, the technique produces IR images with lower contrast, unclear edges, and complex noise under the imaging environment.…”

Section: Introductionmentioning

confidence: 99%

A Novel Adaptive Group Sparse Representation Model Based on Infrared Image Denoising for Remote Sensing Application

Chen

Zhu

et al. 2023

Applied Sciences

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“…Establishing an appropriate vehicle dynamics model plays a very important role in the tracking control accuracy of intelligent vehicles [11][12] . The vehicle dynamics model should not only meet the accuracy but also meet the real-time requirements [13][14] .…”

Section: Vehicle Dynamics Modelmentioning

confidence: 99%

Scheme design of intelligent vehicle auto drive system

Yan,

Wang,

Hao

2023

Third International Conference on Electronics, Electrical and Information Engineering (ICEEIE 2023)

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With the development of urbanization and commercialization, it is an inevitable trend to develop and apply autonomous intelligent vehicles to solve traffic congestion and improve travel efficiency. This paper mainly studies and designs the implementation method of the auto drive system of electric vehicles, focuses on the software and hardware technology scheme of the auto drive system, and studies the technical route of the development and implementation of the auto drive system. The research results can provide some reference for the development and application of the auto drive system of intelligent vehicles.

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“…The infrared imaging guidance system as a complete system composed of several modules, generally can be divided into: moving target detection, target recognition, target acquisition, target tracking is finally to the point of attack for confirmation [1,2]. Moving object detection as a module initial guidance system is the key of the whole system, infrared technology can remote imaging characteristics so often has small moving targets in infrared images obtained, the small moving target detection in infrared image has gradually become one of the important symbols of modern military system, intelligent [3][4][5][6]. Infrared image of the moving small target imaging area is generally much smaller, in the image of complex background, the target is also very likely by the amount of noise, often have low SNR image, these adverse factors make the detection performance of infrared images of small moving target detection method is difficult to be guaranteed [7][8][9][10][11].…”

Section: Introductionmentioning

confidence: 99%