In this paper, we propose a noise-robust online convolutional coding model for image representation, which can use the noisy images as training data. Then an alternating algorithm is utilized to convert the model into two sub-problems, the image pursuit problem and the dictionary learning problem. For the image pursuit problem, the Gauss elimination method is used to solve the equation set which is derived by the Euler equation and discrete Fourier transform. For the dictionary learning problem, a gradient-descent flow is derived to solve it. Experimental results show that our method can output more meaningful feature representations compared to the related models while the training data was corrupted by Poisson noise.

在本文中，我们提出了一种用于图像表示的鲁棒在线卷积编码模型，该模型可以将嘈杂的图像用作训练数据。然后利用交替算法将模型转换为两个子问题，即图像追踪问题和字典学习问题。对于图像追踪问题，采用高斯消去法求解由欧拉方程和离散傅立叶变换导出的方程组。对于字典学习问题，派生了一个梯度下降流来解决它。实验结果表明，与传统模型相比，在泊松噪声破坏训练数据的情况下，该方法可以输出更有意义的特征表示。