Color TV: total variation methods for restoration of vector-valued images

We propose a new definition of the total variation (TV) norm for vector-valued functions that can be applied to restore color and other vector-valued images. The new TV norm has the desirable properties of 1) not penalizing discontinuities (edges) in the image, 2) being rotationally invariant in the image space, and 3) reducing to the usual TV norm in the scalar case. Some numerical experiments on denoising simple color images in red-green-blue (RGB) color space are presented.

[1]  Curtis R. Vogel,et al.  Ieee Transactions on Image Processing Fast, Robust Total Variation{based Reconstruction of Noisy, Blurred Images , 2022 .

[2]  Guillermo Sapiro,et al.  Color Snakes , 1997, Comput. Vis. Image Underst..

[3]  Françoise Dibos,et al.  A morphological scheme for mean curvature motion and applications to anisotropic diffusion and motion of level sets , 1994, Proceedings of 1st International Conference on Image Processing.

[4]  P. Lions,et al.  Axioms and fundamental equations of image processing , 1993 .

[5]  Charles Poynton,et al.  Frequently asked questions about colour , 1995 .

[6]  Jitendra Malik,et al.  Scale-Space and Edge Detection Using Anisotropic Diffusion , 1990, IEEE Trans. Pattern Anal. Mach. Intell..

[7]  Guillermo Sapiro,et al.  Anisotropic diffusion of multivalued images with applications to color filtering , 1996, IEEE Trans. Image Process..

[8]  Curtis R. Vogel,et al.  Fast Total Variation-Based Image Reconstruction , 1995 .

[9]  Silvano Di Zenzo,et al.  A note on the gradient of a multi-image , 1986, Comput. Vis. Graph. Image Process..

[10]  W D Wright,et al.  Color Science, Concepts and Methods. Quantitative Data and Formulas , 1967 .

[11]  Curtis R. Vogel,et al.  Iterative Methods for Total Variation Denoising , 1996, SIAM J. Sci. Comput..

[12]  Yuying Li,et al.  An Affine Scaling Algorithm for Minimizing Total Variation in Image Enhancement , 1994 .

[13]  William F. Schreiber,et al.  Fundamentals of Electronic Imaging Systems , 1986 .

[14]  Tomaso A. Poggio,et al.  On Edge Detection , 1984, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[15]  G. Koepfler,et al.  A morphological scheme for mean curvature motion and applications to anisotropic diffusion and motion of level sets , 1995 .

[16]  Guido Gerig,et al.  Vector-Valued Diffusion , 1994, Geometry-Driven Diffusion in Computer Vision.

[17]  R. LeVeque Numerical methods for conservation laws , 1990 .

[18]  Gene H. Golub,et al.  A Nonlinear primal dual method for TV-based image restoration , 1996 .

[19]  L. Álvarez,et al.  Signal and image restoration using shock filters and anisotropic diffusion , 1994 .

[20]  J. B. Rosen The Gradient Projection Method for Nonlinear Programming. Part I. Linear Constraints , 1960 .

[21]  Aldo Cumani,et al.  Edge detection in multispectral images , 1991, CVGIP Graph. Model. Image Process..

[22]  Hsien-Che Lee,et al.  Detecting boundaries in a vector field , 1991, IEEE Trans. Signal Process..

[23]  R. Nevatia A Color Edge Detector and Its Use in Scene Segmentation , 1977 .

[24]  P. Lions,et al.  Image selective smoothing and edge detection by nonlinear diffusion. II , 1992 .

[25]  Guillermo Sapiro,et al.  Vector-valued active contours , 1996, Proceedings CVPR IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[26]  J. Sethian,et al.  Fronts propagating with curvature-dependent speed: algorithms based on Hamilton-Jacobi formulations , 1988 .

[27]  L. Rudin,et al.  Nonlinear total variation based noise removal algorithms , 1992 .

[28]  Antonin Chambolle,et al.  Partial differential equations and image processing , 1994, Proceedings of 1st International Conference on Image Processing.

[29]  J. B. Rosen The gradient projection method for nonlinear programming: Part II , 1961 .

[30]  Steven J. Ruuth Eecient Algorithms for Diiusion-generated Motion by Mean Curvature , 1997 .