Simultaneous Estimation of Super-resolved Depth and Image from Low Resolution Defocused Observations

This paper presents a novel technique to simultaneously estimate the depth and the focused image of a scene both at a super-resolution, from its defocused observations. Super-resolution refers to the generation of high resolution images from a sequence of low resolution images. Hitherto, the super-resolution technique has been restricted only to the intensity domain. In this paper, we extend the scope of super-resolution to acquire depth estimates at high resolution simultaneously. Given a sequence of low resolution, blurred and noisy observations of a static scene, the problem is to generate a dense depth map at a resolution higher than one that can be generated from the observations as well as to estimate the true focused image. Both the depth as well as the image are modeled as separate Markov Random Fields and a maximum a posteriori method is used to derive a cost function which is then optimized using simulated annealing (SA).