Efficient Wavelet related Transforms in Image Denoising

Abstract

Noise corrupts the image during acquisition and transmission phase. It degrades the quality of the image due to which some important information gets lost. Image denoising is the process of reducing the noise from the noisy image and enhancing the quality of the image while preserving the important details of the image like edges, corners, structure and texture. There are many conventional filters and algorithms available to denoise the noisy images in spatial and frequency domain. This paper focuses on one of the most adaptive and efficient platform where any number of efficient denoising schemes can be developed, i.e. wavelet transform. This is an old concept, but still in much use due to its easy and adaptive behavior. A lot of standard modifications have been done in wavelet transform. This paper briefly introduces those wavelet transforms which are highly efficient in the field of image denoising and also introduces some extended versions of the wavelet which are very popular in the field of image processing and scientific computing. The main motive of this paper is to provide research awareness about the various wavelet transforms in image denoising.