基于残差编解码器的通道自适应超声图像去噪方法

Channel Adaptive Ultrasound Image Denoising Method Based on Residual Encoder-decoder Networks

超声图像去噪对提高超声图像的视觉质量和完成其他相关的计算机视觉任务都至关重要。超声图像中的特征信息与斑点噪声信号较为相似,用已有的去噪方法对超声图像去噪,容易造成超声图像纹理特征丢失,这会对临床诊断的准确性产生严重的干扰。因此,在去除斑点噪声的过程中,需尽量保留图像的边缘纹理信息才能更好地完成超声图像去噪任务。该文提出一种基于残差编解码器的通道自适应去噪模型(RED-SENet),能有效去除超声图像中的斑点噪声。在去噪模型的解码器部分引入注意力反卷积残差块,使本模型可以学习并利用全局信息,从而选择性地强调关键通道的内容特征,抑制无用特征,能提高模型去噪的性能。在2个私有数据集和2个公开数据集上对该模型进行定性评估和定量分析,与一些先进的方法相比,该模型的去噪性能有显著提升,并在噪声抑制以及结构保持方面具有良好的效果。

The denoising of ultrasound images is very important to improve the visual quality of ultrasound images and to accomplish other related computer vision tasks.The feature information in ultrasound images is similar to the speckle noise signal.The existing denoising methods for ultrasound images denoising are easy to cause the loss of texture features of ultrasound images,which will cause serious interference to the accuracy of clinical diagnosis.Therefore,in the process of speckle noise removal,the edge texture information of images should be retained as far as possible to complete better the task of ultrasound images denoising.RED-SENet(Residual Encoder-Decoder with Squeeze-and-Excitation Network),a channel adaptive denoising model based on residual encoder-decoder is presented,which can effectively remove speckle noise in ultrasound images.By introducing the attention deconvolution residual block in the decoder part of the denoising model,the model can learn and use the global information,selective emphasizing the content features of the key channels and suppress the useless features,which can improve the denoising performance of the model.The model is qualitatively evaluated and quantitatively analyzed on 2 private datasets and 2 public datasets,respectively.Compared with some advanced methods,the denoising performance of the model is significantly improved,and it has advantages in noise suppression and structure preservation.