We describe a solution for image restoration in a computational
camera known as an extended depth of field
(EDOF) system. The specially-designed optics produce
point spread functions that are roughly invariant with object distance
in a range. However, this invariance involves a trade-off
with the peak sharpness of the lens. The lens blur
is a function of lens field-height, and the imaging sensor introduces signal-dependent noise. In this context, the principal contributions
of this paper are: a) the modeling of the EDOF focus recovery
problem; and b) the adaptive EDOF focus recovery approach, operating in signal-dependent noise.
The focus recovery solution is adaptive to complexities of an EDOF imaging system,
and performs a joint deblurring and noise
suppression. It also adapts to imaging conditions by accounting for the state of the sensor (e.g., low-light conditions).