A New Fuzzy-Based Wavelet Shrinkage Image Denoising Technique

This paper focuses on fuzzy image denoising techniques. In particular, we investigate the usage of fuzzy set theory in the domain of image enhancement using wavelet thresholding. We propose a simple but efficient new fuzzy wavelet shrinkage method, which can be seen as a fuzzy variant of a recently published probabilistic shrinkage method [1] for reducing adaptive Gaussian noise from digital greyscale images. Experimental results show that the proposed method can efficiently and rapidly remove additive Gaussian noise from digital greyscale images. Numerical and visual observations show that the performance of the proposed method outperforms current fuzzy non-wavelet methods and is comparable with some recent but more complex wavelets methods. We also illustrate the main differences between this version and the probabilistic version and show the main improvements in comparison to it.

• Type: Conference
• Year: 2006
• Where: ACIVS
• Authors: Stefan Schulte, Bruno Huysmans, Aleksandra Pizurica, Etienne E. Kerre, Wilfried Philips
• Keywords: ACIVS 2006, Computer Vision, Digital Greyscale Images, Fuzzy Wavelet Shrinkage, Shrinkage Method