Performance investigation of TDBLMS Adaptive Filter for Noise cancellation

Abstract

Generally, images can be corrupted by different characteristic noises simultaneously, we cannot obtain
satisfactory filtering result if only using single filter such as average filter or median filter. Therefore, in this paper a new
Adaptive filter algorithm is proposed for filtering the image corrupted by difference noises. Firstly, we construct adaptive
structure using neighborhood contrast measure; secondly, divide the image into smoothness, edge and unconfirmed
regions based on the adaptive structure; then, adopt corresponding filter for different regions. The algorithm does not
need a priori knowledge of images and noises. Image denoising involves the manipulation of the image data to produce a
visually high quality image. Different noise models including additive and multiplicative types are used. They include
Gaussian noise, salt and pepper noise and speckle noise. Selection of the denoising algorithm is application dependent.
Hence, it is necessary to have knowledge about the noise present in the image so as to select the appropriate denoising
algorithm.