{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/reweighted-infrared-patch-tensor-model-with","title":"Reweighted Infrared Patch-Tensor Model With Both Non-Local and Local Priors for Single-Frame Small Target Detection","arxiv_id":"1703.09157","date":"2017-03-27","proceeding":null,"authors":["Yimian Dai","Yiquan Wu"],"abstract":"Many state-of-the-art methods have been proposed for infrared small target\ndetection. They work well on the images with homogeneous backgrounds and\nhigh-contrast targets. However, when facing highly heterogeneous backgrounds,\nthey would not perform very well, mainly due to: 1) the existence of strong\nedges and other interfering components, 2) not utilizing the priors fully.\nInspired by this, we propose a novel method to exploit both local and non-local\npriors simultaneously. Firstly, we employ a new infrared patch-tensor (IPT)\nmodel to represent the image and preserve its spatial correlations. Exploiting\nthe target sparse prior and background non-local self-correlation prior, the\ntarget-background separation is modeled as a robust low-rank tensor recovery\nproblem. Moreover, with the help of the structure tensor and reweighted idea,\nwe design an entry-wise local-structure-adaptive and sparsity enhancing weight\nto replace the globally constant weighting parameter. The decomposition could\nbe achieved via the element-wise reweighted higher-order robust principal\ncomponent analysis with an additional convergence condition according to the\npractical situation of target detection. Extensive experiments demonstrate that\nour model outperforms the other state-of-the-arts, in particular for the images\nwith very dim targets and heavy clutters.","url_abs":"http://arxiv.org/abs/1703.09157v1","url_pdf":"http://arxiv.org/pdf/1703.09157v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"reweighted-infrared-patch-tensor-model-with","repo_url":"https://github.com/YimianDai/DENTIST","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1703.09157","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}