Hybrid Fourth Order Generalized Diffusion

Abstract: 

Although there exists a wide variety of methods for removing additive Gaussian noise, relatively few works tackle the problem of removing mixed noise type from images. In this work, we utilize a new hybrid partial differential equation (PDE) model for mixed noise corrupted images. We do a mathematical analysis of PDEs  and perform experimental results for noise removal on synthetic and real imagery.  

Comparison with Classical Schemes

Left to right: Noisy, Perona-Malik, Total Variation, ABO [3], and our ABO4 [1].

Reference:

[1] V. B. S. Prasath$, P. Kalavathi. Mixed noise removal using hybrid fourth order mean curvature motion. 2nd International Symposium on Signal Processing and Intelligent Recognition Systems (SIRS), Trivandrum, India. Proc. Springer Advances in Intelligent Systems and Computing 425 (Eds.: S. M. Thampi, S. Bandyopadhyay, S. Krishnan, K.-C. Li, S. Mosin and M. Ma), pp. 625-632, December 2015. doi:10.1007/978-3-319-28658-7_53

[2] V. B. Prasath, D. Vorotnikov. On fourth order mean curvature motion from image restoration.  In preparation, 2024. Preliminary version at arXiv. doi:10.48550/arXiv.24xx.abcde

Acknowledgment:

$This work was done while the author was at the IPAM, University of California Los Angeles, CA, USA. The author thanks the IPAM institute for their great hospitality and support during the visit.

Bibliography:

[3] S. Kim. PDE-based image restoration: a hybrid model and color image denoising. IEEE Transactions on Image Processing, pp. 1163-1170, Vol. 15, No. 5, 2006.

For Vectorial (= Color, Multispectral & Hyperspectral) image processing projects please visit this page.