A fuzzy similarity measure-based hybrid image filter (FHF) is proposed for color image restoration in this paper. Operation is carried out in three steps: parameter optimization, hybrid image filter setup, and image restoration. For parameter optimization, a multimethodology evolutionary computation (MMEC) is presented for real-parameter optimization problems. Then, FHF with a fuzzy-based similarity measure is introduced for noise reduction. Finally, a color image is restored with an experience-based construction of FHF which has been optimized via MMEC. Experimental results show the proposed FHF achieves a high peak signal-to-noise ratio and mean structural similarity by effectively reducing Gaussian, impulse, and mixed-noise.