Differentiable Imaging: Progress, Challenges, and Outlook

Differentiable imaging has emerged as a transformative paradigm in computational imaging by enabling end-to-end optimization of complex computational-optical systems. This framework bridges the gap between physical reality and computational models through differentiable programming, addressing long-standing challenges in uncertainty quantification, system design, and computational complexity. We review recent advances that demonstrate how differentiable imaging enables simultaneous optimization of physical and computational elements, enhances reconstruction accuracy, and expands imaging capabilities beyond traditional limits. We examine how this approach systematically addresses both deterministic and stochastic uncertainties while enabling novel system co-design strategies. The review analyzes emerging challenges in numerical modeling, computational efficiency, and system integration, exploring how digital twin architectures may offer promising solutions for next-generation imaging systems. Looking ahead, we discuss key opportunities and fundamental questions in theoretical foundations, system innovation, and scientific applications that will shape the future of differentiable imaging.