Nội dung được dịch bởi AI, chỉ mang tính chất tham khảo
Mô hình phục hồi ảnh xám dựa trên PDE bậc bốn tuyến tính
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 địnhTài 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