Automatic blur type classification via ensemble SVM

• Published in: Signal processing. Image communication Vol. 71; pp. 24 - 35
• Main Authors: Wang, Rui, Li, Wei, Li, Rui, Zhang, Liang
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier B.V 01.02.2019; Elsevier BV
• Subjects: Basis functions; Blur image classification; Classifiers; Communication; Computer simulation; Digital imaging; Domains; Ensemble SVM classifier; Feature ranking; Feature selection; Flicker; Haze; Image classification; Image restoration; Optimization; Radial basis function; Random sampling; Recursive methods; Signal processing; State of the art; Support vector machine-recursive feature elimination (SVM-RFE); Support vector machines; Support vector rate (SVR); Websites; Ensemble SVM classifier; Support vector machine-recursive feature elimination (SVM-RFE); Feature selection; Support vector rate (SVR); Feature ranking; Blur image classification

Automatic classification of blur type is critical to blind image restoration. In this paper, we propose an original solution for blur type classification of digital images using ensemble Support Vector Machine (SVM) structure. It is assumed that each image is subject to at most one of three blur typ...