Literature Review of Wavelet-Based Image Filtering Methods

Noise is a random signal that corrupts the quality of an image. For many decades, eliminating it or reducing its intensity in the image has been a major concern for many scientists. To this end, several studies on image processing (with the aim of eliminating or reducing the intensity of noise) have been carried out. Although a multitude of filtering methods emerged from these studies, numerous works have shown the effectiveness of the wavelet transform in image filtering. Thus, several wavelet-based methods (for image filtering purposes) have been illustrated in the literature. Some methods involve performing wavelet decomposition followed by thresholding and other methods involve combining wavelet decomposition with various filtering methods. This combination can have two or more methods. After filtering, the effectiveness of the filter can be evaluated. It is measured in terms of qualitative and/or quantitative parameters. Indeed, a filtering method is said to be efficient when the PSNR parameter (parameter most used in the literature) obtained has a minimum value of 30dB. This article presents a literature review of wavelet-based image filtering methods. Emphasis is placed on works presenting a better PSNR value (wavelet type, combination carried out, mother wavelet, decomposition number). In the end, this article gives a panoramic view of the choices of tools to use by a reader who would like to get involved in image filtering