{"data":{"id":"10.6084/m9.figshare.1590018","type":"dois","attributes":{"doi":"10.6084/m9.figshare.1590018","prefix":"10.6084","suffix":"m9.figshare.1590018","identifiers":[],"alternateIdentifiers":[],"creators":[{"name":"Yan, Hao","givenName":"Hao","familyName":"Yan","affiliation":[],"nameIdentifiers":[]},{"name":"Paynabar, Kamran","givenName":"Kamran","familyName":"Paynabar","affiliation":[],"nameIdentifiers":[]},{"name":"Shi, Jianjun","givenName":"Jianjun","familyName":"Shi","affiliation":[],"nameIdentifiers":[]}],"titles":[{"title":"Anomaly Detection in Images With Smooth Background via Smooth-Sparse Decomposition"}],"publisher":"Taylor \u0026 Francis","container":{},"publicationYear":2017,"subjects":[{"subject":"Space Science"},{"subject":"Medicine"},{"subject":"Pharmacology"},{"subject":"Sociology"},{"subject":"FOS: Sociology","schemeUri":"http://www.oecd.org/science/inno/38235147.pdf","subjectScheme":"Fields of Science and Technology (FOS)"},{"subject":"Biological Sciences not elsewhere classified"},{"subject":"Information Systems not elsewhere classified"},{"subject":"Mathematical Sciences not elsewhere classified"}],"contributors":[],"dates":[{"date":"2017-01-31","dateType":"Created"},{"date":"2023-06-02","dateType":"Updated"},{"date":"2017","dateType":"Issued"}],"language":null,"types":{"ris":"DATA","bibtex":"misc","citeproc":"dataset","schemaOrg":"Dataset","resourceType":"Dataset","resourceTypeGeneral":"Dataset"},"relatedIdentifiers":[{"relationType":"IsSupplementTo","relatedIdentifier":"10.1080/00401706.2015.1102764","relatedIdentifierType":"DOI"}],"relatedItems":[],"sizes":["289638 Bytes"],"formats":[],"version":null,"rightsList":[{"rights":"Creative Commons Attribution 4.0 International","rightsUri":"https://creativecommons.org/licenses/by/4.0/legalcode","schemeUri":"https://spdx.org/licenses/","rightsIdentifier":"cc-by-4.0","rightsIdentifierScheme":"SPDX"}],"descriptions":[{"description":"In various manufacturing applications such as steel, composites, and textile production, anomaly detection in noisy images is of special importance. Although there are several methods for image denoising and anomaly detection, most of these perform denoising and detection sequentially, which affects detection accuracy and efficiency. Additionally, the low computational speed of some of these methods is a limitation for real-time inspection. In this article, we develop a novel methodology for anomaly detection in noisy images with smooth backgrounds. The proposed method, named smooth-sparse decomposition, exploits regularized high-dimensional regression to decompose an image and separate anomalous regions by solving a large-scale optimization problem. To enable the proposed method for real-time implementation, a fast algorithm for solving the optimization model is proposed. Using simulations and a case study, we evaluate the performance of the proposed method and compare it with existing methods. Numerical results demonstrate the superiority of the proposed method in terms of the detection accuracy as well as computation time. This article has supplementary materials that includes all the technical details, proofs, MATLAB codes, and simulated images used in the 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