Epsilon Focus Photography: A Study of Focus, Defocus and Depth-of-field
Focus, defocus and depth-of-field are integral aspects of a photograph captured using a wide-aperture camera. Focus and defocus blur provide critical cues for estimation of scene depth and structure which helps in scene understanding or post-capture image manipulation. Focus and defocus blur are also used creatively by photographers to produce remarkable compositional effects such as emphasis on the foreground subject with aesthetic bokeh in the background. Epsilon Focus Photography is a branch of computational photography that deals with the capture and processing of multi-focus imagery - where multiple wide-aperture images are captured with a small change in focus position. In this thesis, we provide a comprehensive study of various problems in epsilon focus photography along with a detailed analysis of the related work in the area. We provide useful constructs for the understanding and manip- ulation of focus, defocus blur and the depth-of-field of an image. The work in this thesis can be divided into four broad categories of measurement, representation, manipulation and applications of focus. Measuring focus is a long studied and challenging problem in computer vision. We study various methods to measure focus and propose a composite measure of focus that combines the strengths of well-known focus measures. We the study the task of post-capture focus manipulation at each pixel in an image and formulate a novel representation of focus that can find much use in image editing toolkits. Our representation can faithfully encode the fine characteristics of a wide-aperture image even at complex interaction locations such as depth-edges and over-saturated background regions, while optimizing the memory footprint of multi-focus imagery. Apart from precise geometric constructs for scene refocusing, we also propose an data-driven approach for post-capture scene refocusing using deep adversarial learning. We show how the tasks of deblurring an image, magnification of the defocused content and overall comprehensive focus manipulation can be efficiently modeled using conditional adversarial networks. We study several applications of focus in computer vision such as view interpolation and depth-from-focus. We provide a tool that can interpolate different views of a scene based on focus texture segmentation and propose a novel solution for depth-from-focus using the proposed composite focus measure. In summary, this thesis consists of a comprehensive study of epsilon focus photography and its applications in the context of computer vision and computational photography.
|Year of completion:||August 2021|
|Advisor :||P J Narayanan|