Fourth-Order Anisotropic Diffusion for Inpainting and Image Compression

Author:

Jumakulyyev Ikram,Schultz Thomas

Abstract

AbstractEdge-enhancing diffusion (EED) can reconstruct a close approximation of an original image from a small subset of its pixels. This makes it an attractive foundation for PDE based image compression. In this work, we generalize second-order EED to a fourth-order counterpart. It involves a fourth-order diffusion tensor that is constructed from the regularized image gradient in a similar way as in traditional second-order EED, permitting diffusion along edges, while applying a non-linear diffusivity function across them. We show that our fourth-order diffusion tensor formalism provides a unifying framework for all previous anisotropic fourth-order diffusion based methods, and that it provides additional flexibility. We achieve an efficient implementation using a fast semi-iterative scheme. Experimental results on natural and medical images suggest that our novel fourth-order method produces more accurate reconstructions compared to the existing second-order EED.

Publisher

Springer International Publishing

Cited by 3 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Deep spatial and tonal data optimisation for homogeneous diffusion inpainting;Pattern Analysis and Applications;2023-04-08

2. Combining Image Space and q-Space PDEs for Lossless Compression of Diffusion MR Images;Journal of Mathematical Imaging and Vision;2023-03-28

3. Lossless PDE-based Compression of 3D Medical Images;Lecture Notes in Computer Science;2021

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