We develop a new estimation technique for recovering depth-of-field from
multiple stereo images. Depth-of-field is estimated by determining the shift in
image location resulting from different camera viewpoints. When this shift is
not divisible by pixel width, the multiple stereo images can be combined to
form a super-resolution image. By modeling this super-resolution image as a
realization of a random field, one can view the recovery of depth as a
likelihood estimation problem.