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Smartphone cameras today are increasingly approaching the versatility andquality of professional cameras through a combination of hardware and softwareadvancements. However, fixed aperture remains a key limitation, preventingusers from controlling the depth of field (DoF) of captured images. At the sametime, many smartphones now have multiple cameras with different fixed apertures-- specifically, an ultra-wide camera with wider field of view and deeper DoFand a higher resolution primary camera with shallower DoF. In this work, wepropose $\text{DC}^2$, a system for defocus control for synthetically varyingcamera aperture, focus distance and arbitrary defocus effects by fusinginformation from such a dual-camera system. Our key insight is to leveragereal-world smartphone camera dataset by using image refocus as a proxy task forlearning to control defocus. Quantitative and qualitative evaluations onreal-world data demonstrate our system's efficacy where we outperformstate-of-the-art on defocus deblurring, bokeh rendering, and image refocus.Finally, we demonstrate creative post-capture defocus control enabled by ourmethod, including tilt-shift and content-based defocus effects.

$\text{DC}^2$: Dual-Camera Defocus Control by Learning to Refocus