Infrared Dim Small Target Detection Technology Based on RPCA

Abstract

In this paper, a novel small target detection algorithm based on robust master analysis (RPCA) is proposed to solve the problem of small target difficult to detection in single infrared image. Because of the background matrix is a low rank matrix and the target matrix is a sparse matrix, small target detection can be formulated as an optimization problem of recovering low-rank and sparse matrices. RPCA method is used to restore the background matrix and targets matrix, and then choose the inexact augmented Lagrange multipliers to solve RPCA model which is faster. At last, two simulation experiments result indicate that can adapt to a variety of single image and the timeliness is good for small matrix.

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

“…In order to be able to effectively illustrate the different processing effects of the algorithm proposed in this paper and other algorithms, this section uses the dataset in the literature [33] as the scene A of this paper's experiments, one scene collected by the team as the scene B of this paper's experiments, and then one of the dataset in the literature [34] as the scene C of this paper's experiments, in terms of the improved bilateral filtering [6], the gradient weighted filtering model [15], the TDLMS filtering [12], anisotropic filtering [8], RPCA model [28], PSTNN model [36], FRKW model [37] and the algorithm proposed in this paper are compared, and the original images of the three experimental scenes, the difference images of each algorithm after background suppression and the difference images in…”

“…Low-rank sparse algorithm for background prediction mainly uses the low-rank property of the image to decompose the image to find the rank [25], so as to obtain the low-rank part of the image and the sparse part to complete the background prediction, and such algorithms need to train the samples of the image and then compose the feature dictionary, which makes the sparse matrix of the relevant calculation extremely complicated and increases the operation cycle of the algorithm, and such algorithms are mainly for single-frame image and the algorithm mainly deals with single-frame images, but requires a large dictionary of feature information to support multi-frame sequence images, which greatly slows down the efficiency of detection. But compared with traditional algorithms, these algorithms have a greater advantage in background suppression and can detect target points accurately, and their typical algorithms are IPI model [26], TV-PCP model [27], RPCA model [28,29], etc. The IPI model proposed by Gao et al obtains the background image and the difference image by decomposing the image for rank [26], which is closely related to the moving step and filter window size, and in the actual filtering process, the optimal step and window for different images are inconsistent, and the parameters need to be adjusted to obtain the optimal parameters, which in summary again reflects the applicability of the IPI model to single-frame detection.…”

“…The IPI model proposed by Gao et al obtains the background image and the difference image by decomposing the image for rank [26], which is closely related to the moving step and filter window size, and in the actual filtering process, the optimal step and window for different images are inconsistent, and the parameters need to be adjusted to obtain the optimal parameters, which in summary again reflects the applicability of the IPI model to single-frame detection. The algorithm of robust principal component analysis proposed by Fan et al [28] also achieves better results in background suppression, by using the high correlation between backgrounds to reduce the dimensionality of the image to recover the low-rank and sparse parts of the image to complete the background suppression. However, the same algorithm is also affected by the moving step and filtering window, and is not applicable to the detection of sequential images.…”

Data Enhancement via Low-Rank Matrix Reconstruction in Pulsed Thermography for Carbon-Fibre-Reinforced Polymers

“…Infrared dim small target detection has been a hot and difficult research topic in infrared search and tracking systems. Later, Fan et al [14] introduced a novel detection algorithm based on RPCA to solve the difficulty of small target detection.…”

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

“…For such detection scenarios, researchers have proposed many feasible detection algorithms. 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.…”