Mô hình phục hồi ảnh xám dựa trên PDE bậc bốn tuyến tính

Springer Science and Business Media LLC - Tập 38 - Trang 1-21 - 2019
B. V. Rathish Kumar1, Abdul Halim1
1Department of Mathematics and Statistics, Indian Institute of Technology Kanpur, Kanpur, India

Tóm tắt

Trong bài báo này, chúng tôi sẽ trình bày một mô hình phục hồi ảnh dựa trên PDE biến thiên, trong đó chúng tôi đã sử dụng bình phương của chuẩn $$L^2$$ của Hessian của ảnh u như một thành phần điều chỉnh. Phương trình Euler–Lagrange sẽ dẫn chúng tôi đến một PDE tuyến tính bậc bốn. Đối với phân rời thời gian, chúng tôi đã sử dụng phân tách lồi và sơ đồ nửa rời kết quả được giải quyết trong miền Fourier. Phân tích ổn định cho sơ đồ nửa rời được thực hiện. Chúng tôi sẽ trình bày một số kết quả số và so sánh với $$\text {TV}-L^2$$ và $$\text {TV}-H^{-1}$$ mô hình.

Từ khóa

#PDE biến thiên #phục hồi ảnh #điều chỉnh Hessian #phương trình Euler–Lagrange #phương trình đạo hàm riêng bậc cao #phân tách lồi #miền Fourier #phân tích ổn định

Tài liệu tham khảo

Aubert G, Kornprobst P (2006) Mathematical problems in image processing: partial differential equations and the calculus of variations, vol 147. Springer, Bwelin Bertalmio M, Sapiro G, Caselles V, Ballester C (2000) Image inpainting. In: Proceedings of the 27th annual conference on Computer graphics and interactive techniques (SIGGRAPH ’00), New Orleans, LU, pp 417–424 Bertalmio M, Bertozzi AL, Sapiro G (2001) Navier–Stokes, fluid dynamics, and image and video inpainting. In: Proceedings of the 2001 IEEE computer society conference on computer. Vision and pattern recognition. CVPR 2001, vol 1, pp 355–362 Bertozzi AL, Esedoglu S, Gillette A (2007a) Inpainting of binary images using the Cahn–Hilliard equation. IEEE Trans Image Process 16(1):285–291 Bertozzi AL, Esedoglu S, Gillette A (2007b) Analysis of a two-scale Cahn–Hilliard model for image inpainting. Multiscale Model Simul 6(3):913–936 Burger M, He L, Schönlieb CB (2009) Cahn–Hilliard inpainting and a generalization for grayvalue images. SIAM J Imaging Sci 2(4):1129–1167 Chan TF, Shen J (2001a) Mathematical models for local non-texture inpaintings. SIAM J Appl Math 62(3):1019–1043 Chan TF, Shen J (2001b) Non-texture inpainting by curvature driven diffusions (CDD). J Vis Commun Image Rep 12(4):436–449 Chan TF, Kang SH, Shen J (2002) Euler’s elastica and curvature-based inpainting. SIAM J Appl Math 63(2):564592 Chan TF, Shen JH, Zhou HM (2006) Total variation wavelet inpainting. J Math Imaging Vis 25(1):107–125 Cherfils L, Fakih H, Miranville A (2017) A complex version of the Cahn–Hilliard equation for grayscale image inpainting. Multiscale Model Simul 15:575–605 Criminisi A, Perez P, Toyama K (2003) Object removal by exemplar-based inpainting. IEEE Int Conf Comput Vis Pattern Recognit 2:721–728 Deo SG, Lakshmikantham V, Raghavendra V (1997) Textbook of ordinary differential equations. Tata McGraw-Hill, New York Dobrosotskaya JA, Bertozzi AL (2008) A wavelet-laplace variational technique for image deconvolution and inpainting. IEEE Trans Image Process 17(5):657–663 Efros AA, Leung TK (1999) Texture synthesis by non-parametric sampling. In: IEEE international conference on computer vision, Corfu, Greece Esedoglu S, Shen JH (2002) Digital inpainting based on the Mumford–Shah–Euler image model. Eur J Appl Math 13(4):353–370 Evans LC (2010) Partial differential equations. Graduate studies in mathematics. American Mathematical Society, Providence Eyre D (1998) An unconditionally stable one-step scheme for gradient systems. Unpublished Fife PC (2000) Models for phase separation and their mathematics. Electron J Differ Equ 48:1–26 Gillette A (2006) Image Inpainting using a modified Cahn–Hilliard equation. PhD thesis, University of California, Los Angeles Kašpar R, Zitová B (2003) Weighted thin-plate spline image denoising. Pattern Recognit 36:3027–3030 Li X (2011) Image recovery via hybrid sparse representations: a deterministic annealing approach. IEEE J Sel Top Signal Process 5(5):953–962 Masnou S, Morel J (1998) Level lines based disocclusion. In: 5th IEEE international conference on image processing, Chicago, pp 259–263 Mumford D, Shah J (1989) Optimal approximations by piecewise smooth functions and associated variational problems. Commun Pure Appl Math 42:577–685 Nitzberg N, Mumford D, Shiota T (1993) Filtering, segmentation, and depth. Lecture notes in computer science. Springer, Berlin Papafitsoros K, Schönlieb CB, Sengul B (2013) Combined first and second order total variation inpainting using split Bregman. Image Process. Line 3:112136 Perona P, Malik J (1990) Scale-space and edge detection using anisotropic diffusion. IEEE Trans Pattern Anal Mach Intell 12(7):629–639 Rudin LI, Osher S, Fatemi E (1992) Nonlinear total variation based noise removal algorithms. Physica D 60:259–268 Sch önlieb CB (2009) Modern PDE techniques for image inpainting. PhD thesis, DAMTP, University of Cambridge Schönlieb, (2012) Higher-order total variation inpainting. File Exchange, MATLAB Central Schönlieb CB, Bertozzi A (2011) Unconditionally stable schemes for higher order inpainting. Commun Math Sci 9(2):413–457 Temam R (1997) Infinite dimensional dynamical systems in mechanics and physics, vol 68. Springer, Berlin Theljani A, Belhachmi Z, Kallel M, Moakher M (2017) Multiscale fourth order model for image inpainting and low-dimensional sets recovery. Math Methods Appl Sci 40:3637–3650 Vijayakrishna R (2015) A unified model of Cahn–Hilliard greyscale inpainting and multiphase classification. PhD thesis, IIT Kanpur, India Wang Z, Bovik AC (2002) A universal image quality index. IEEE Signal Process Lett 9(3):81–84