Details

Autor(en) / Beteiligte

• Chen, Ni
• Cao, Liangcai
• Poon, Ting‐Chung
• Lee, Byoungho
• Lam, Edmund Y.

Titel

Differentiable Imaging: A New Tool for Computational Optical Imaging

Ist Teil von

• Advanced Physics Research, 2023-06, Vol.2 (6), p.n/a

Ort / Verlag

Wiley-VCH

Erscheinungsjahr

2023

Link zum Volltext

Quelle

Alma/SFX Local Collection

Beschreibungen/Notizen

• The field of computational imaging has made significant advancements in recent years, yet it still faces limitations due to the restrictions imposed by traditional computational techniques. Differentiable programming offers a solution by combining the strengths of classical optimization and deep learning, enabling the creation of interpretable model‐based neural networks. Through the integration of physics into the modeling process, differentiable imaging, which employs differentiable programming in computational imaging, has the potential to overcome challenges posed by sparse, incomplete, and noisy data. As a result, it has the potential to play a key role in advancing the field of computational imaging and its various applications. Differentiable imaging, which integrates differentiable programming into computational imaging, offers a powerful solution that leverages the strengths of both classical optimization and deep neural networks. With its capability to incorporate physics into the model, it has the potential to overcome limitations posed by the complexity of imaging systems and the restrictions of computational techniques, and therefore has the potential to drive the continued advancement of computational imaging.