A restoration–segmentation algorithm based on flexible Arnoldi–Tikhonov method and Curvelet denoising. Issue 5 (July 2016)

• Record Type: Journal Article
• Authors: Aghazadeh, Nasser; Akbarifard, Farideh; Cigaroudy, Ladan
• Is Part Of: Signal, image and video processing. Volume 10:Issue 5(2016)
• Page Start: 935
• Page End: 942
• Publication Date: 2016-07
• Subjects: Image restoration -- Image segmentation -- Arnoldi–Tikhonov -- Curvelet -- Thresholding -- Hessian matrix; Signal processing -- Digital techniques -- Periodicals; Image processing -- Digital techniques -- Periodicals; Digital video -- Periodicals; 621.3822
• DOI: 10.1007/s11760-015-0843-8
• Languages: English
• ISSNs: 1863-1703

Abstract

Abstract In this paper, we illustrate a two-stage algorithm consisting of restoration and segmentation to reach binary segmentation from the noisy and blurry image. The results of our method can be applied in main fields of the image processing such as object extraction. In the first stage, we have a linear discrete ill-posed problem with a noise-contaminated right-hand side, arising from the image restoration. We consider problems in which the coefficient matrix is the sum of Kronecker products of matrices and present a global flexible Arnoldi–Tikhonov method coupled with the generalized cross-validation for the computation of the regularization parameter at each iteration. The proposed algorithm is based on the global Arnoldi method that allows using a more flexible solution subspace. In the second stage, we segment the restored image in order to reach a binary image in which the target object is emphasized. In our segmentation method, we use Gaussian scale-space technique to compute discrete gradients of the restored image for pre-segmenting. Also, in order to denoise, we use a tight frame of Curvelet transforms and thresholding which is based on the principle obtained by minimizing Stein's unbiased risk estimate. This algorithm has an iterative part based on the iterative part of TFA (Cai et al. in SIAM J Imaging 6(1):464–486, 2013), but we use eigenvectors of Hessian matrix of image for improving this part. Theoretical properties of the method of both stages and numerical experiments are presented.