Multi- Noise Reduction Technique Using Triangular Fuzzy Membership Function

Image noise is a random shift in the brightness or color information in images, and it is sometimes confused with electrical noise. It is an unwanted by-product of image capture that obscures the information that should be seen. De-noising contaminated images through the use of essential image processing technologies in various methods, resulting in the retrieval of lost information to improve and analyze images greatly. A fuzzy-based logic is proposed in this paper as a method for effective image filtering; specifically, it is used to de-noise an image by replacing noisy pixels with non-noisy ones based on the degree of membership of neighboring pixels within a filter mask. Results from experiments show that the triangular fuzzy-based membership filter outperforms competing filters in removing multiple kinds of image noise, including Speckle noise, Gaussian noise, Salt & Paper noise, and Poisson noise.