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Found 6 papers, 1 papers with code
Trainable Joint Bilateral Filters for Enhanced Prediction Stability in Low-dose CT
Low-dose computed tomography (CT) denoising algorithms aim to enable reduced patient dose in routine CT acquisitions while maintaining high image quality.
Limited Parameter Denoising for Low-dose X-ray Computed Tomography Using Deep Reinforcement Learning
The tuning is carried out by two deep CNNs.
Ultra Low-Parameter Denoising: Trainable Bilateral Filter Layers in Computed Tomography
Due to the extremely low number of trainable parameters with well-defined effect, prediction reliance and data integrity is guaranteed at any time in the proposed pipelines, in contrast to most other deep learning-based denoising architectures.
Low Dose Helical CBCT denoising by using domain filtering with deep reinforcement learning
The results and the performances of our approach overtake the results of the other previous methods.
Low Dose CT Denoising via Joint Bilateral Filtering and Intelligent Parameter Optimization
Denoising of clinical CT images is an active area for deep learning research.
JBFnet -- Low Dose CT Denoising by Trainable Joint Bilateral Filtering
JBFnet is split into four filtering blocks, each of which performs Joint Bilateral Filtering.
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