Revisiting Autofocus for Smartphone Cameras
Autofocus (AF) on smartphones is the process of determining how to move a camera's lens such that certain scene content is in focus. The underlying algorithms used by AF systems, such as contrast detection and phase differencing, are well established. However, determining a high-level objective regarding how to best focus a particular scene is less clear. This is evident in part by the fact that different smartphone cameras employ different AF criteria; for example, some attempt to keep items in the center in focus, others give priority to faces while others maximize the sharpness of the entire scene. The fact that different objectives exist raises the research question of whether there is a preferred objective. This becomes more interesting when AF is applied to videos of dynamic scenes. The work in this paper aims to revisit AF for smartphones within the context of temporal image data. As part of this effort, we describe the capture of a new 4D dataset that provides access to a full focal stack at each time point in a temporal sequence. Based on this dataset, we have developed a platform and associated application programming interface (API) that mimic real AF systems, restricting lens motion within the constraints of a dynamic environment and frame capture. Using our platform we evaluated several high-level focusing objectives and found interesting insight into what users prefer. We believe our new temporal focal stack dataset, AF platform, and initial user-study findings will be useful in advancing AF research.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Synthetic Defocus and Look-Ahead Autofocus for Casual Videography
In cinema, large camera lenses create beautiful shallow depth of field (DOF), but make focusing difficult and expensive. Accurate cinema focus usually relies on a script and a person to control focus in realtime. Casual …
BIG-bench Machine LearningSaliency DetectionAutofocus for Event Cameras
Focus control (FC) is crucial for cameras to capture sharp images in challenging real-world scenarios. The autofocus (AF) facilitates the FC by automatically adjusting the focus settings. However, due to the lack of effe…
Learning to Autofocus
Autofocus is an important task for digital cameras, yet current approaches often exhibit poor performance. We propose a learning-based approach to this problem, and provide a realistic dataset of sufficient size for effe…
Depth EstimationOne-Step Event-Driven High-Speed Autofocus
High-speed autofocus in extreme scenes remains a significant challenge. Traditional methods rely on repeated sampling around the focus position, resulting in ``focus hunting''. Event-driven methods have advanced focusing…
Defocus Map Estimation and Deblurring from a Single Dual-Pixel Image
We present a method that takes as input a single dual-pixel image, and simultaneously estimates the image's defocus map -- the amount of defocus blur at each pixel -- and recovers an all-in-focus image. Our method is ins…
Deblurring