Premium
Defocus Magnification
Author(s) -
Bae Soonmin,
Durand Frédo
Publication year - 2007
Publication title -
computer graphics forum
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.578
H-Index - 120
eISSN - 1467-8659
pISSN - 0167-7055
DOI - 10.1111/j.1467-8659.2007.01080.x
Subject(s) - depth of field , computer vision , artificial intelligence , computer science , magnification , lens (geology) , image (mathematics) , point (geometry) , kernel (algebra) , contrast (vision) , mathematics , optics , physics , geometry , combinatorics
A blurry background due to shallow depth of field is often desired for photographs such as portraits, but, unfortunately, small point‐and‐shoot cameras do not permit enough defocus because of the small diameter of their lenses. We present an image‐processing technique that increases the defocus in an image to simulate the shallow depth of field of a lens with a larger aperture.Our technique estimates the spatially‐varying amount of blur over the image, and then uses a simple image‐based technique to increase defocus. We first estimate the size of the blur kernel at edges and then propagate this defocus measure over the image. Using our defocus map, we magnify the existing blurriness, which means that we blur blurry regions and keep sharp regions sharp. In contrast to more difficult problems such as depth from defocus, we do not require precise depth estimation and do not need to disambiguate textureless regions.