WAVELET-BASED CONVOLUTIONAL NEURAL NETWORK DESIGN WITH ANAPPLICATION TO DUAL-ENERGY CBCT PRE-SPECTRAL-DECOMPOSITION FILTERING

Convolutional Neural Networks (CNNs) are reshaping signal processing and computer vision by providing data-driven solutions for inverse problems such as noise reduction. However, their relationship with established signal processing methods is sometimes unclear and its development not fully exploiting the existing knowledge. In this paper, rather than improving existing CNNs with wavelet transformations as explored earlier, we improve the wavelet shrinkage approach to noise-reduction with a data-driven solution. The resulting CNN has clear encoding, decoding and processing paths. As application, we perform noise reduction in Dual-Energy Cone-Beam CT. The obtained results were compared to a UNet-like architecture, which reveal better noise-free images without aliasing artifacts. This indicates that that our architecture is able to preserve well the information contained in the images because the architecture exploits explicitly the underlying signal representation.