A novel multiresolution pyramidal edge detector, based on adaptive weighted fuzzy mean(AWFM)filtering and fuzzy linking model, is presented in this paper. The algorithm first constructs a pyramidal structure by repetitive AWFM filtering and subsampling of original image. Then it utilizes multiple heuristic linking criteria between the edge nodes of two adjacent levels and considers the linkage as a fuzzy model, which is trained offline. Through this fuzzy linking model, the boundaries detected at coarse resolution are propagated and refined to the bottom level from the coarse-to fine edge detection. The validation experiment results demonstrate that the proposed approach has superior performance compared with standard fixed resolution detector andprevious multiresolution approach, especially in impulse noise environment. A novel multiresolution pyramidal edge detector, based on adaptive weighted fuzzy mean (AWFM) filtering and fuzzy linking model, is presented in this paper. The algorithm first constructs a pyramidal structure by repetitive AWFM filtering and subsampling of original image. Then it called multiple heuristic linking criteria between the edge nodes of two adjacent levels and considers the linkage as a fuzzy model, which is trained offline. Through this fuzzy linking model, the boundaries detected at coarse resolution are propagated and refined to the bottom level from the coarse-to fine edge validation. The validation experiment results demonstrate that the proposed approach has superior performance compared with standard fixed resolution detector and previous multiresolution approach, especially in impulse noise environment.