$\text{DC}^2$: Dual-Camera Defocus Control by Learning to Refocus
This addresses the problem of limited depth-of-field control for smartphone users, offering an incremental improvement through software-based defocus manipulation.
The paper tackles the limitation of fixed apertures in smartphone cameras by proposing DC^2, a system that uses dual-camera data to synthetically control defocus effects, outperforming state-of-the-art methods in tasks like defocus deblurring and bokeh rendering.
Smartphone cameras today are increasingly approaching the versatility and quality of professional cameras through a combination of hardware and software advancements. However, fixed aperture remains a key limitation, preventing users from controlling the depth of field (DoF) of captured images. At the same time, many smartphones now have multiple cameras with different fixed apertures -- specifically, an ultra-wide camera with wider field of view and deeper DoF and a higher resolution primary camera with shallower DoF. In this work, we propose $\text{DC}^2$, a system for defocus control for synthetically varying camera aperture, focus distance and arbitrary defocus effects by fusing information from such a dual-camera system. Our key insight is to leverage real-world smartphone camera dataset by using image refocus as a proxy task for learning to control defocus. Quantitative and qualitative evaluations on real-world data demonstrate our system's efficacy where we outperform state-of-the-art on defocus deblurring, bokeh rendering, and image refocus. Finally, we demonstrate creative post-capture defocus control enabled by our method, including tilt-shift and content-based defocus effects.