Speckle noises inherently exist in Synthetic Aperture Radar (SAR) image. It reduces the quality of the image and must be removed. Recently, several fuzzy based noise filters have been proposed and it is claimed that they are more superior to the existing state of the art classical filters. However, most of these filters are designed for removing impulse noise. This paper presents a new fuzzy weighted mean filter for SAR images. The weights are computed using fuzzy rules. These rules can differentiate variation in pixel values due to noise and edges. In edge region, the neighboring pixels are assigned with less weight, and thereby preserving the edge pixels. The value of parameters used in defining fuzzy membership functions is determined using Particle Swarm Optimization (PSO) technique. The proposed fuzzy filter is comparatively assessed using Equivalent Number of Looks (ENL), Mean of Ratio (MoR), Signal-to-Noise Ratio (SNR), and Edge-Preservation Factor (EPF) with some of the existing noise removal techniques. It is found that the proposed filter can suppress speckle noise present in SAR images and simultaneously preserve the image details.
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