Stable Autofocus with Focal Consistency Loss

Autofocus aims to accurately position the camera lens to bring the desired region of interest into focus. Conventional works search for the sharpest frame within the lens movement. However, sharpness measure in many real-world settings is ambiguous and may cause a focus hunting problem, where the lens continuously moves back and forth to search for the accurate position. To mitigate this problem, we introduce a simple yet powerful loss function, specifically designed to produce consistent outputs in autofocus systems. The proposed Focal Consistency Loss (FCL) allows auto-focus models to better learn the geometric cues relative to each initial position of the lens, significantly reducing distracting lens movement and enhancing the user experience when taking a photo. Furthermore, we improve autofocus stability by utilizing multiple consecutive frames in a practical way. Experimental results show the effectiveness of FCL in various practical scenarios, including multi-frame autofocus for both conventional and dual-pixel images.

Authors

Institutions

Publication Details

Published
2025-02-26
DOI
https://doi.org/10.1109/wacv61041.2025.00072
Citations
2
Primary Topic
Image Processing Techniques and Applications
Type
article
Field-Weighted Citation Impact
2.27
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Stable Autofocus with Focal Consistency Loss

Nagyeong Lee, Myungsub Choi, Hyong-Euk Lee, Sangwon Lee
2 citations
Image Processing Techniques and Applications
2.27
article

Stable Autofocus with Focal Consistency Loss

Nagyeong Lee, Myungsub Choi, Hyong-Euk Lee, Sangwon Lee
article en
2 citations

Abstract

Autofocus aims to accurately position the camera lens to bring the desired region of interest into focus. Conventional works search for the sharpest frame within the lens movement. However, sharpness measure in many real-world settings is ambiguous and may cause a focus hunting problem, where the lens continuously moves back and forth to search for the accurate position. To mitigate this problem, we introduce a simple yet powerful loss function, specifically designed to produce consistent outputs in autofocus systems. The proposed Focal Consistency Loss (FCL) allows auto-focus models to better learn the geometric cues relative to each initial position of the lens, significantly reducing distracting lens movement and enhancing the user experience when taking a photo. Furthermore, we improve autofocus stability by utilizing multiple consecutive frames in a practical way. Experimental results show the effectiveness of FCL in various practical scenarios, including multi-frame autofocus for both conventional and dual-pixel images.

Samsung (South Korea) (KR)
Openalex Percentile: Top 12%
Image Processing Techniques and Applications
2.27
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Stable Autofocus with Focal Consistency Loss — Nagyeong Lee, Myungsub Choi, et al. · (2025) | TGRS Research Map | TGRS