Depth from Focusing and Defocusing

This paper studies the problem of obtaining depth information from focusing and defocusing, which have long been noticed as important sources of depth information for human and machine vision. In depth from focusing, we try to eliminate the local maxima problem which is the main source of inaccuracy in focusing; in depth from defocusing, a new computational model is proposed to achieve higher accuracy. The major contributions of this paper are: (1) In depth from focusing, instead of the popular Fibonacci search which is often trapped in local maxima, we propose the combination of Fibonacci search and curve fitting, which leads to an unprecedentedly accurate result; (2) New model of the blurring effect which takes the geometric blurring as well as the imaging blurring into consideration, and the calibration of the blurring model; (3) In spectrogram-based depth from defocusing, a maximal resemblance estimation method is proposed to decrease or eliminate the window effect. This paper repo..