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.

中文翻译：

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OCDL-ACDF：一种基于自适应复数域字典的复数值图像去噪方法

降噪是复数值图像应用中必不可少的预处理程序。提出了一种基于复合值字典学习和自适应复合值字典滤波器（OCDL-ACDF）的复合值图像去噪方法。我们的字典首先通过在线字典学习方法进行训练。然后，为了进一步减少字典原子中包含的噪声，我们基于冗余字典原子之间的特征相似性设计了一个复值字典滤波器。通过结合在线字典学习和实值图像去噪方法的优势，获得了有效的复值字典。在稀疏编码过程中，采用了贪婪算法即正交匹配跟踪方法。仿真实验表明，该方法的去噪效果不仅优于目前的先进算法，而且在避免过度拟合方面也很有效。细节保真度也相对较高。