Solving ill-posed inverse problems using iterative deep neural networks

Source: Dev.to

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

This new approach tackles hard imaging puzzles that normally produce noisy, blurry results. It blends known physics with learning from examples so the method can fix images step by step while preserving what we already know about how the data is generated.

The result is a loop that uses a deep network to steer each step, improving details where classic methods 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 dB improvement.

Performance

• Generates 512 × 512 images in roughly 0.4 seconds on a single GPU.
• Provides clearer images without discarding the underlying physics.
• Learns only the necessary information, letting math and data work together for quicker, often better results than traditional tricks, with few extra steps needed.

Further reading

Read the comprehensive review on Paperium.net:
Solving ill‑posed inverse problems using iterative deep neural networks