Multilevel-DWT based image de-noising using feed forward artificial neural network
Torali Saikia, Kandarpa Kumar Sarma · 2014
It is seen that during image acquisition, storage, retrieval or transmission, images get degraded due to presence of noise. With different varieties of noise and its extent, de-noising becomes challenging. Traditionally, a host of techniques have considered spatial, statistical and multiple domain approaches for de-noising. Yet, the scope always exist for exploring innovative means of performing de-noising for enhancing image quality. In the proposed work, we present an approach to de-noise images by combining the features of multilevel Discrete Wavelet Transform (DWT) and Feed Forward Artificial Neural Network (FF ANN). We apply our algorithm to de-noise the images corrupted by a kind of multiplicative noise known as speckle noise. The results show that the proposed method proves effective for a range of variations and is suitable for critical applications.