Aiming at the problem in fuzzy image enhancement, we proposed a dual chaotic quantum particle swarm optimization algorithm. Firstly, an additional contraction expansion factor is added to the quantum particle swarm to dynamically change the search boundary. Then the dual chiotic quantum mechanism system uses two different chaotic mechanisms to independently search in the search space, and according to the distance between the optimal points obtained with two mechanisms, the search space is narrowed and the true optimal value can be got. Incomplete Beta function is adopted finally to astablish the relationsip between the dual chaotic quantum particle swarm optimization and fuzzy image enhancement recovery relationship. The experimental simulation shows a clear recovery effect of the proposed algorithm, and the overall visual effect of the image is fairly good. In comparison with other algorithms, the histogram shows that the proposed algorithm results in evenly distributed gray values and can significantly improve the signal-to-noise ratio.