Ultra Low-Parameter Denoising: Trainable Bilateral Filter Layers in Computed Tomography. (arXiv:2201.10345v1 [eess.IV])

Web: http://arxiv.org/abs/2201.10345

Computed tomography is widely used as an imaging tool to visualize
three-dimensional structures with expressive bone-soft tissue contrast.
However, CT resolution and radiation dose are tightly entangled, highlighting
the importance of low-dose CT combined with sophisticated denoising algorithms.
Most data-driven denoising techniques are based on deep neural networks and,
therefore, contain hundreds of thousands of trainable parameters, making them
incomprehensible and prone to prediction failures. Developing understandable
and robust denoising algorithms achieving state-of-the-art performance helps to
minimize radiation dose while …

arxiv