In recent years, iris recognition has been widely used in various fields. As the first step of iris recognition, segmentation accuracy is of great significance to the final recognition. However, iris images exhibit a variety of noise in the real world, which leads to lower segmentation accuracy than the ideal case. To address this problem, this paper proposes an iris segmentation method using feature channel optimization for noisy images. The method for non-ideal environments with noise is more suitable for practical applications. We add dense blocks and dilated convolutional layers to the encoder so that the information gradient flow obtained by different layers can be reused, and the receptive field can be expanded. In the decoder, based on Jensen-Shannon (JS) divergence, we first recalculate the weight of the feature channels obtained from each layer, which enhances the useful information and suppresses the interference information in the noisy environments to boost the segmentation accuracy. The proposed architecture is validated in the CASIA v4.0 interval (CASIA) and IIT Delhi v1.0 datasets (IITD). For CASIA, the mean error rate is 0.78%, and the F-measure value is 98.21%. For IITD, the mean error rate is 0.97%, and the F-measure value is 97.87%. Experimental results show that the proposed method outperforms other state-of-art methods under noisy environments, such as Gaussian blur, Gaussian noise, and salt and pepper noise.

近年来，虹膜识别已广泛用于各个领域。作为虹膜识别的第一步，分割精度对最终识别具有重要意义。然而，虹膜图像在现实世界中表现出各种噪声，这导致分割精度低于理想情况。为了解决这个问题，本文提出了一种基于特征通道优化的噪声图像虹膜分割方法。对于具有噪声的非理想环境，该方法更适合实际应用。我们在编码器中添加了密集块和扩张的卷积层，以便可以重用不同层获得的信息梯度流，并可以扩展接收场。在解码器中，基于詹森·香农（JS）散度，我们首先重新计算从每一层获得的特征通道的权重，以增强有用信息并抑制嘈杂环境中的干扰信息，从而提高分割精度。在CASIA v4.0间隔（CASIA）和IIT Delhi v1.0数据集（IITD）中对提出的体系结构进行了验证。对于CASIA，平均错误率为0.78％，F量度值为98.21％。对于IITD，平均错误率为0.97％，F量度值为97.87％。实验结果表明，该方法在噪声环境下优于其他最新方法，例如高斯模糊，高斯噪声以及盐和胡椒噪声。在CASIA v4.0间隔（CASIA）和IIT Delhi v1.0数据集（IITD）中对提出的体系结构进行了验证。对于CASIA，平均错误率为0.78％，F量度值为98.21％。对于IITD，平均错误率为0.97％，F量度值为97.87％。实验结果表明，该方法在噪声环境下优于其他最新方法，例如高斯模糊，高斯噪声以及盐和胡椒噪声。在CASIA v4.0间隔（CASIA）和IIT Delhi v1.0数据集（IITD）中对提出的体系结构进行了验证。对于CASIA，平均错误率为0.78％，F量度值为98.21％。对于IITD，平均错误率为0.97％，F量度值为97.87％。实验结果表明，该方法在噪声环境下优于其他最新方法，例如高斯模糊，高斯噪声以及盐和胡椒噪声。