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Cameras were originally designed using physics-based heuristics to captureaesthetic images. In recent years, there has been a transformation in cameradesign from being purely physics-driven to increasingly data-driven andtask-specific. In this paper, we present a framework to understand the buildingblocks of this nascent field of end-to-end design of camera hardware andalgorithms. As part of this framework, we show how methods that exploit bothphysics and data have become prevalent in imaging and computer vision,underscoring a key trend that will continue to dominate the future oftask-specific camera design. Finally, we share current barriers to progress inend-to-end design, and hypothesize how these barriers can be overcome.

Physics vs. Learned Priors: Rethinking Camera and Algorithm Design for Task-Specific Imaging