Ultrasound is a widely used medical tool for non-invasive diagnosis, but its
images often contain speckle noise which can lower their resolution and
contrast-to-noise ratio. This can make it more difficult to extract, recognize,
and analyze features in the images, as well as impair the accuracy of
computer-assisted diagnostic techniques and the ability of doctors to interpret
the images. Reducing speckle noise, therefore, is a crucial step in the
preprocessing of ultrasound images. Researchers have proposed several speckle
reduction methods, but no single method takes all relevant factors into
account. In this paper, we compare seven such methods: Median, Gaussian,
Bilateral, Average, Weiner, Anisotropic and Denoising auto-encoder without and
with skip connections in terms of their ability to preserve features and edges
while effectively reducing noise. In an experimental study, a convolutional
noise-removing auto-encoder with skip connection, a deep learning method, was
used to improve ultrasound images of breast cancer. This method involved adding
speckle noise at various levels. The results of the deep learning method were
compared to those of traditional image enhancement methods, and it was found
that the proposed method was more effective. To assess the performance of these
algorithms, we use three established evaluation metrics and present both
filtered images and statistical data.

通过对七种不同的减噪方法进行比较，本文提出了一种使用深度学习方法的自动编码器来降噪超声图像的方法，并将其与传统的图像增强方法进行了对比，结果发现该方法更为有效。

利用带有跳跃连接的去噪自编码器进行超声图像中的斑点噪声降低

Speckle Noise Reduction in Ultrasound Images using Denoising  Auto-encoder with Skip Connection

Decreased visibility, intensive noise, and biased color are the common
problems existing in low-light images. These visual disturbances further reduce
the performance of high-level vision tasks, such as object detection, and
tracking. To address this issue, some image enhancement methods have been
proposed to increase the image contrast. However, most of them are implemented
only in the spatial domain, which can be severely influenced by noise signals
while enhancing. Hence, in this work, we propose a novel residual recurrent
multi-wavelet convolutional neural network R2-MWCNN learned in the frequency
domain that can simultaneously increase the image contrast and reduce noise
signals well. This end-to-end trainable network utilizes a multi-level discrete
wavelet transform to divide input feature maps into distinct frequencies,
resulting in a better denoise impact. A channel-wise loss function is proposed
to correct the color distortion for more realistic results. Extensive
experiments demonstrate that our proposed R2-MWCNN outperforms the
state-of-the-art methods quantitively and qualitatively.

提出一种新颖的残差循环多小波卷积神经网络 R2-MWCNN 用于低光图像增强和降噪，该网络通过多层离散小波变换将输入特征图划分为不同的频率，提出了通道损失函数来更正颜色扭曲，实验证明该方法在量化和定性上优于现有的方法。