结合迁移学习的真实图像去噪算法

为了能有效地去除真实图像的复杂噪声,提出了一种结合迁移学习的真实图像去噪算法。该算法采用了双编码器结构,迁移学习编码单元利用预先训练好的权值有效提取鲁棒特征,残差编码单元对当前数据处理,进一步补充了信息。解码单元通过特征融合模块对丰富的信息进行融合,随后经过残差注意力模块加强对图像细节信息的关注,从而更好地恢复图像。实验结果表明,该算法在DND、SIDD和RNI15真实噪声数据集上有很好的泛化能力,能够在有效去除噪声的同时更好地保留图像纹理和边缘信息,恢复图像视觉效果更好。

Real-world Images Denoising Algorithm Combined with Transfer Learning

To effectively remove the complex noise from real-world images, an image denoising algorithm combined with transfer learning is proposed. The algorithm was built with a dual encoder structure, in which the transfer learning coding unit effectively extracted robust features using pre-trained weights and the residual coding unit processed the current data. Combining the dual encoders further supplemented the information. The decoding unit fused the rich information by a feature fusion module and subsequently passed to a residual attention module to enhance the attention on image detail information so as to better restore images. Experimental results demonstrate that the algorithm achieves excellent generalization on DND, SIDD, and RNI15 real-world noise datasets. The algorithm can effectively remove noise while preserving image textures and edge information to restore images with better visual effects.