The DOI matches its Crossref record; 33 of the 33 references it cites were found in a registry or an index.
Infrared Small Target Detection via Non-Convex Rank Approximation Minimization Joint l2,1 Norm
Open accessAbstract
To improve the detection ability of infrared small targets in complex backgrounds, a novel method based on non-convex rank approximation minimization joint l2,1 norm (NRAM) was proposed. Due to the defects of the nuclear norm and l1 norm, the state-of-the-art infrared image-patch (IPI) model usually leaves background residuals in the target image. To fix this problem, a non-convex, tighter rank surrogate and weighted l1 norm are instead utilized, which can suppress the background better while preserving the target efficiently. Considering that many state-of-the-art methods are still unable to fully suppress sparse strong edges, the structured l2,1 norm was introduced to wipe out the strong residuals. Furthermore, with the help of exploiting the structured norm and tighter rank surrogate, the proposed model was more robust when facing various complex or blurry scenes. To solve this non-convex model, an efficient optimization algorithm based on alternating direction method of multipliers (ADMM) plus difference of convex (DC) programming was designed. Extensive experimental results illustrate that the proposed method not only shows superiority in background suppression and target enhancement, but also reduces the computational complexity compared with other baselines.
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Cites
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Model Selection and Estimation in Regression with Grouped Variables
Yuan, M., & Lin, Y. (2006) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.1467-9868.2005.00532.x · Cited by 7,261
Weighted Nuclear Norm Minimization with Application to Image Denoising
Gu, S., Zhang, L., Zuo, W., & Feng, X. (2014) · 2014 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2014.366 · Cited by 2,316 · cited 4×
“The existing single-frame-based detection algorithms mainly fall into three main groups: methods of background-based assumption, methods of target-based assumption, and methods of target and background separating.”
BackgroundMethodInfluentialSelf-citationWright, J. N., Ganesh, A., Rao, S., Peng, Y., & Ma, Y. (2009) · Illinois Digital Environment for Access to Learning and Scholarship (University of Illinois at Urbana-Champaign) · Cited by 1,495
Infrared Patch-Image Model for Small Target Detection in a Single Image
Gao, C., Meng, D., Yang, Y., Wang, Y., Zhou, X., & Hauptmann, A. G. (2013) · IEEE Transactions on Image Processing · doi:10.1109/tip.2013.2281420 · Cited by 1,380
A Local Contrast Method for Small Infrared Target Detection
Chen, C. L. P., Li, H., Wei, Y., Xia, T., & Tang, Y. Y. (2014) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2013.2242477 · Cited by 1,351
The Augmented Lagrange Multiplier Method for Exact Recovery of Corrupted Low-Rank Matrices
Lin, Z., Chen, M., & Ma, Y. (2010) · Illinois Digital Environment for Access to Learning and Scholarship (University of Illinois at Urbana-Champaign) · doi:10.48550/arxiv.1009.5055 · Cited by 1,176
Max-mean and max-median filters for detection of small targets:
Deshpande, S. D., Er, M. H., Venkateswarlu, R., & Chan, P. (1999) · SPIE Proceedings · doi:10.1117/12.364049 · Cited by 853
Fast and Accurate Matrix Completion via Truncated Nuclear Norm Regularization
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Weighted Nuclear Norm Minimization and Its Applications to Low Level Vision
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Self-citation
Cited by
Infrared Small Target Detection Based on Partial Sum of the Tensor Nuclear Norm
Zhang, L., & Peng, Z. (2019) · Remote Sensing · doi:10.3390/rs11040382 · Cited by 759
Self-citationISNet: Shape Matters for Infrared Small Target Detection
Zhang, M., Zhang, R., Yang, Y., Bai, H., Zhang, J., & Guo, J. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52688.2022.00095 · Cited by 721
Zhang, T., Li, L., Cao, S., Pu, T., & Peng, Z. (2023) · IEEE Transactions on Aerospace and Electronic Systems · doi:10.1109/taes.2023.3238703 · Cited by 486
Self-citationDFFIR-net: Infrared Dim Small Object Detection Network Constrained by Gray-level Distribution Model
Yang, Z., Ma, T., Ku, Y., Ma, Q., & Fu, J. (2022) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2022.3220285 · Cited by 299
Metadata
- Authors
- Zhang, L., Peng, L., Zhang, T., Cao, S., & Peng, Z.
- Venue
- Remote Sensing
- Publisher
- MDPI AG
- Published
- 2018-11-16
- Volume
- 10
- Issue
- 11
- Pages
- 1821
- ISSN
- 2072-4292 (ISSN-L)
- OpenAlex
- W2900678331
- Cited by
- 484
- References
- 33
- FWCI
- 14.16
- Type
- journal article
- Language
- en
- Open access
- gold
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