Least-squares shape from defocus

Abstract

The problem of inferring the 3-D shape and radiance of a scene from blurred images can be posed as the minimization of a cost functional, as we have seen in Section 3.6. There we have shown that the cost functional derived from the assumption that the noise is additive and Gaussian yields a least-squares formulation of the problem of shape from defocus. This assumption is not necessarily the most realistic, but it yields a particularly simple solution that separates the estimate of shape (shape from defocus) from that of radiance (image restoration). In this chapter we explore this approach; although based on questionable assumptions, it results in particularly simple, intuitive, and instructive algorithms. The reader should revisit Section 2.1.4 where we introduce the operator notation that we use extensively in this chapter.