A new technique for edge detection in color images corrupted by Gaussian noise is presented. The proposed method adopts a multipass processing approach that gradually reduces the noise in the R, G, and B components of the image. The prefiltering steps are specifically designed to operate in conjunction with the edge detection algorithm. They adopt two different models for data smoothing that aim at avoiding false edges produced by noise and at preserving the image details during noise removal. The subsequent algorithm for edge detection has been designed to further decrease the sensitivity to noise of the overall method. Thus, accurate edge maps can be achieved even in the presence of highly corrupted data. Results of computer simulations show that the proposed approach significantly improves our previous methods and performs better than other techniques in the literature.