Noise Analysis and Removal Using Fuzzy Mean Filter with Triangular Membership Function

In digital Image Processing, removal of noise is a highly demanded area of research. Impulsive noise is common in images which arise at the time of image acquisition or transmission of images. A lot of research works have been done on the restoration of images corrupted by impulse noise. However, due to some limitations of the filters under different conditions the edge preservable efficiency is less. In this paper, we propose an image filtering technique by using Fuzzy mean filter based on fuzzy entropy given by triangular membership function to remove impulse noise of corrupted images. The proposed method is based on noise detection, noise removal and edge preservation modules. The proposed algorithm is capable to reconstruct 90% of the damaged image. The results will be evaluated with various parameters such as PSNR, SSIM, NAE, NCC and SCC so that we can ensure about the quality of services and Fuzzy Mean Filter for Immense Impulse Noise Removal (FMIINR) proves to be very robust at immense noise level. The main advantage of the proposed technique over the other filtering techniques is its superior noise removal as well as features of image preserving capability.