Image Denoising Using Fractal Hierarchical Classification

Authors

Swalpa Kumar Roy, Jalpaiguri Government Engineering College
Nilavra Bhattacharya, The University of Texas at Austin
Bhabatosh Chanda, Indian Statistical Institute, Kolkata
Bidyut B. Chaudhuri, Indian Statistical Institute, Kolkata
Soumitro Banerjee, Indian Institute of Science Education and Research Kolkata

Document Type

Conference Article

Publication Title

Communications in Computer and Information Science

Abstract

This paper proposes an efficient yet simple fractal-based image denoising technique. Denoising is carried out during fractal coding process. Hierarchical classification is used to increase encoding speed, and avoid a lot of futile mean-square-error (MSE) computations. Quadtree-based image partitioning using dynamic range and domain sizes is used to increase the degree of noise removal. Further denoising is achieved using pyramidal decoding, using non-arbitrary seed image, and additional post processing. Results from experiments show that our proposed scheme improves the structural similarity (SSIM) index of the Lenna image from 44% to 78% for low noise cases, and from 9% to 35% for high noise cases.

First Page

631

Last Page

645

DOI

10.1007/978-981-13-1343-1_52

Publication Date

1-1-2018

Recommended Citation

Roy, Swalpa Kumar; Bhattacharya, Nilavra; Chanda, Bhabatosh; Chaudhuri, Bidyut B.; and Banerjee, Soumitro, "Image Denoising Using Fractal Hierarchical Classification" (2018). Conference Articles. 145.
https://digitalcommons.isical.ac.in/conf-articles/145