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Found 4 papers, 0 papers with code
SDIP: Self-Reinforcement Deep Image Prior Framework for Image Processing
SDIP efficiently utilizes this trait in a reinforcement learning manner, where the current iteration's output is utilized by a steering algorithm to update the network input for the next iteration, guiding the algorithm toward improved results.
A Global Constraint to Improve CT Reconstruction Under Non-Ideal Conditions
Conclusions: Different from most current constraints focusing on the local prior, our proposed constraint only utilizes the global prior of CT images.
Residual Back Projection With Untrained Neural Networks
In this paper, we present a framework for iterative reconstruction (IR) in CT that leverages the hierarchical structure of neural networks, without the need for training.
Gram filtering and sinogram interpolation for pixel-basis in parallel-beam X-ray CT reconstruction
The key aspect of parallel-beam X-ray CT is forward and back projection, but its computational burden continues to be an obstacle for applications.
