Solving ill-posed inverse problems using iterative deep neural networks

Faster, clearer CT images by mixing math and a trained network

This new approach tackles hard imaging puzzles that normally give noisy, blurry results.
It blends known physics with learning from examples so the method can fix images step by step while keeping what we already know about how data is made.
The result is a loop that uses a deep network to steer each step, improving details where classic ways fail on ill-posed problems.
Tests on simple phantoms and a head CT show much sharper pictures than older reconstructions, and it even beats a common method by about 5.
4 dB improvement.
Best part, the process is fast — it makes 512×512 images in about 0.
4 seconds on a single GPU, so it's useful for real work.
You get clearer images without throwing away the physics, and the system learns only what it needs.
It feels like letting math and data work together, producing results quicker and often better than traditional tricks, with few extra steps needed.

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Solving ill-posed inverse problems using iterative deep neural networks

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