Blur parameter locus curve and its applications. Issue 2 (1st February 2020)

• Record Type: Journal Article
• Authors: Kumar, Himanshu; Gupta, Sumana; Subramanian, Venkatesh K.
• Is Part Of: IET image processing. Volume 14:Issue 2(2020)
• Page Start: 297
• Page End: 309
• Publication Date: 2020-02-01
• Subjects: image denoising -- image restoration -- optical transfer function -- image processing; blur parameter locus curve -- point spread function -- characteristic function -- optical system -- BPLC -- system representation -- PSF -- measurement function -- blur kernels -- system characteristics -- point source; Image processing -- Periodicals; 621.36705
• DOI: 10.1049/iet-ipr.2019.0577
• Languages: English
• ISSNs: 1751-9659

Abstract

Abstract : Conventionally, the point spread function (PSF) is understood as a characteristic function of any optical system. It captures the information about the amount of blur present along all the directions for a point in the scene. However, the dependence of blur on the PSF is in the form of convolution for any object other than a point source present in the scene and hence their relationship is less explicit. The authors propose a blur parameter locus curve (BPLC) as a system representation which has a one to one relationship with blur. BPLC simply is a chart of blur amounts in all directions of a given PSF with respect to the selected measurement function. They further characterise the PSF by decomposing the variation of BPLC across all directions based on the study performed for different possible forms of the blur kernels. Such decomposition provides powerful tools for various analysis. As PSF can be anisotropic, the computation of BPLC becomes an essential intermediate step to obtain the scale map as at the same scale, blur is different in different directions. Furthermore, they demonstrate the use of BPLC to obtain other system characteristics function such as PSF.