Image de-noising of a metal matrix composite microstructure using Sure-let wavelet and weighted bilateral filter

Analysis of material microstructure images have been an important research topic. In fact, multiple pertinent information can be extracted from material texture images such as inclusions, fraction surfaces, heterogeneous components, crystallographic planes orientation, porosity and so on. Actually, this information depends from the amount of noise included in the image. Thus, it is very important to enhance methods exploited for image de-nosing in such cases. In fact, images issued from scanned electronic microscopy as well as computed tomography are used in multiple security applications such as baggage screening and in the encrypted domain, it used to protect privacy of outsourced data in cloud computing. In this paper, we propose a comparative study between two popular techniques used in image de-noising that are; Sure-let wavelet technique and weighted bilateral filtering. Our goal is to grasp the versatility of those methods in de-noising microstructure composite material images. Results of both outputs are well discussed. In our case, we found that sure-let wavelet de-noising gives better output results quantitatively and qualitatively.