Noise reduction is an essential preprocess in applications of complex-valued images. A method of complex-valued image denoising based on complex-valued dictionary learning and an adaptive complex-valued dictionary filter (OCDL-ACDF) is proposed. Our dictionary is first trained by the online dictionary learning method. Then, to further reduce the noise contained in the dictionary atoms, we design a complex-valued dictionary filter based on the feature similarity between the atoms of redundant dictionaries. By combining the advantages of online dictionary learning and denoising methods of real-valued images, an effective complex-valued dictionary is obtained. The orthogonal matching tracking method, which is a greedy algorithm, is used in the process of sparse coding. The simulation experiments show that the denoising effect of the proposed method is not only better than the current advanced algorithms but also effective at avoiding overfitting. The detail fidelity was also relatively high.