RED-MAM: A residual encoder-decoder network based on multi-attention fusion for ultrasound image denoising

Abstract

The dependence of speckle noise in ultrasound images on image data makes the research of ultrasound image denoising tasks a great challenge. Several deep learning-based ultrasound image denoising methods have been proposed, but almost all of them suffer from the inability to focus on the importance of feature information on multiple domains at the same time. Therefore, this paper proposes a residual encoder-decoder based on multi-attention fusion attention module (RED-MAM) for ultrasound image denoising, which consists of five convolution layers, five deconvolution layers and two multi-attention fusion attention blocks. In order to make the denoising model better adapted to the properties of speckle noise, a multi-attention fusion attention block is constructed by several different attention modules. The multi-attention fusion attention block is introduced into the residual encoder-decoder denoising network to make the model focus on the ultrasound image texture structure information on multiple domains, thus enhancing the denoising effect of the model on ultrasound images. The performance of our proposed model is evaluated on three ultrasound image datasets. In terms of quantitative metrics, RED-MAM shows substantial improvement in Peak Signal-to-Noise Ratio (PSNR), Structural Similarity (SSIM) and Root Mean Square Error (RMSE). The qualitative results show that RED-MAM has significantly improved denoising performance and has good results in noise suppression as well as structure retention. Our method achieves state-of-the-art performance on all three ultrasound datasets.