WiTUnet: A U-Shaped Architecture Integrating CNN and Transformer for
  Improved Feature Alignment and Local Information Fusion
Bin Wang Fei Deng Peifan Jiang Shuang Wang Xiao Han Hongjie Zheng
Low-dose computed tomography (LDCT) has become the technology of choice for
diagnostic medical imaging, given its lower radiation dose compared to standard
CT, despite increasing image noise and potentially affecting diagnostic
accuracy. To address this, advanced deep learning-based LDCT denoising
algorithms have been developed, primarily using Convolutional Neural Networks
(CNNs) or Transformer Networks with the Unet architecture. This architecture
enhances image detail by integrating feature maps from the encoder and decoder
via skip connections. However, current methods often overlook enhancements to
the Unet architecture itself, focusing instead on optimizing encoder and
decoder structures. This approach can be problematic due to the significant
differences in feature map characteristics between the encoder and decoder,
where simple fusion strategies may not effectively reconstruct images.In this
paper, we introduce WiTUnet, a novel LDCT image denoising method that utilizes
nested, dense skip pathways instead of traditional skip connections to improve
feature integration. WiTUnet also incorporates a windowed Transformer structure
to process images in smaller, non-overlapping segments, reducing computational
load. Additionally, the integration of a Local Image Perception Enhancement
(LiPe) module in both the encoder and decoder replaces the standard multi-layer
perceptron (MLP) in Transformers, enhancing local feature capture and
representation. Through extensive experimental comparisons, WiTUnet has
demonstrated superior performance over existing methods in key metrics such as
Peak Signal-to-Noise Ratio (PSNR), Structural Similarity (SSIM), and Root Mean
Square Error (RMSE), significantly improving noise removal and image quality.