The existing system available for fuzzy noise reduction filters refers to fat-tail noise such as impulse noise and the median filter. Only the reduction of impulse noise uses fuzzy filters. Gaussian noise is not particularly concentrated; It does not distinguish the local variation due to the noise and due to the structure of the image. The proposed system presents a new technique for filtering narrow tail noise and medium narrow tail noise using a diffuse filter. The system first estimates a "diffuse derivative" to be less sensitive to local variations due to image structures such as edges. Second, the membership functions are adapted accordingly to the noise level to perform "diffuse smoothing". A new fuzzy filter is presented for the reduction of noise of damaged images with additive noise. The filter consists of two stages. The first step calculates a diffuse derivative for eight different directions. The second stage uses these fuzzy derivatives to perform diffuse smoothing by weighting the contributions of neighboring pixel values. Both stages are based on fuzzy rules that make use of membership functions. The filter can be applied iteratively and effectively reduce heavy noise. In particular, the form of the membership functions is adapted according to the level of noise remaining after each iteration, making use of the distribution of homogeneity in the image. A statistical model for the distribution of noise can be incorporated to relate homogeneity to the scheme of adaptation of the membership functions. Experimental results are obtained to show the feasibility of the proposed approach.
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