article

Interactive local adjustment of tonal values

Authors:

Dani Lischinski,

Matt Uyttendaele,

Richard SzeliskiAuthors Info & Claims

ACM Transactions on Graphics (TOG), Volume 25, Issue 3

Pages 646 - 653

https://doi.org/10.1145/1141911.1141936

Published: 01 July 2006 Publication History

Abstract

This paper presents a new interactive tool for making local adjustments of tonal values and other visual parameters in an image. Rather than carefully selecting regions or hand-painting layer masks, the user quickly indicates regions of interest by drawing a few simple brush strokes and then uses sliders to adjust the brightness, contrast, and other parameters in these regions. The effects of the user's sparse set of constraints are interpolated to the entire image using an edge-preserving energy minimization method designed to prevent the propagation of tonal adjustments to regions of significantly different luminance. The resulting system is suitable for adjusting ordinary and high dynamic range images, and provides the user with much more creative control than existing tone mapping algorithms. Our tool is also able to produce a tone mapping automatically, which may serve as a basis for further local adjustments, if so desired. The constraint propagation approach developed in this paper is a general one, and may also be used to interactively control a variety of other adjustments commonly performed in the digital darkroom.

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Azizzadeh Mehmandost Olya BMohebian RBagheri HMahdavi Hezaveh AKhan Mohammadi A(2024)Toward real-time fracture detection on image logs using deep convolutional neural network YOLOv5Interpretation10.1190/INT-2022-0104.112:2(SB9-SB18)Online publication date: 25-Mar-2024
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Lv CZhang DGeng SWu ZHuang H(2024)Color Transfer for Images: A SurveyACM Transactions on Multimedia Computing, Communications, and Applications10.1145/363515220:8(1-29)Online publication date: 9-Jul-2024
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Index Terms

Interactive local adjustment of tonal values
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision tasks
  2. Computer graphics
2. Human-centered computing
  1. Human computer interaction (HCI)
    1. Interaction devices
      1. Graphics input devices

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Information

Published In

cover image ACM Transactions on Graphics

ACM Transactions on Graphics Volume 25, Issue 3

July 2006

742 pages

ISSN:0730-0301

EISSN:1557-7368

DOI:10.1145/1141911

Issue’s Table of Contents

Copyright © 2006 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

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Publication History

Published: 01 July 2006

Published in TOG Volume 25, Issue 3

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Cited By

Azizzadeh Mehmandost Olya BMohebian RBagheri HMahdavi Hezaveh AKhan Mohammadi A(2024)Toward real-time fracture detection on image logs using deep convolutional neural network YOLOv5Interpretation10.1190/INT-2022-0104.112:2(SB9-SB18)Online publication date: 25-Mar-2024
https://doi.org/10.1190/INT-2022-0104.1
Lv CZhang DGeng SWu ZHuang H(2024)Color Transfer for Images: A SurveyACM Transactions on Multimedia Computing, Communications, and Applications10.1145/363515220:8(1-29)Online publication date: 9-Jul-2024
https://dl.acm.org/doi/10.1145/3635152
Kim HLee K(2024)Learning Controllable ISP for Image EnhancementIEEE Transactions on Image Processing10.1109/TIP.2023.330581633(867-880)Online publication date: 2024
https://doi.org/10.1109/TIP.2023.3305816
Kar ADhara SSen DBiswas P(2024)Self-Supervision via Controlled Transformation and Unpaired Self-Conditioning for Low-Light Image EnhancementIEEE Transactions on Instrumentation and Measurement10.1109/TIM.2024.337077973(1-13)Online publication date: 2024
https://doi.org/10.1109/TIM.2024.3370779
Tang HLiu GQian YWang JXiong J(2024)EgeFusion: Towards Edge Gradient Enhancement in Infrared and Visible Image Fusion With Multi-Scale TransformIEEE Transactions on Computational Imaging10.1109/TCI.2024.336939810(385-398)Online publication date: 2024
https://doi.org/10.1109/TCI.2024.3369398
Qu HLiu J(2024)A Method for Deblurring Estimation From a Single Image2024 4th International Conference on Consumer Electronics and Computer Engineering (ICCECE)10.1109/ICCECE61317.2024.10504228(51-54)Online publication date: 12-Jan-2024
https://doi.org/10.1109/ICCECE61317.2024.10504228
Xia ZHao JLi KTian ADu Z(2024)Edit propagation via color palettesComputers & Graphics10.1016/j.cag.2024.01.002119(103875)Online publication date: Apr-2024
https://doi.org/10.1016/j.cag.2024.01.002
Du ZZhou JXia ZSeng BXu K(2024)Palette-Based Content-Aware Image RecoloringComputational Visual Media10.1007/978-981-97-2092-7_12(240-258)Online publication date: 10-Apr-2024
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Gong BWang YHan XDou QEl Saddik AMei TCucchiara RBertini MTobon Vallejo DAtrey PHossain M(2023)RecolorNeRF: Layer Decomposed Radiance Fields for Efficient Color Editing of 3D ScenesProceedings of the 31st ACM International Conference on Multimedia10.1145/3581783.3611957(8004-8015)Online publication date: 26-Oct-2023
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Ramos-Soto OOliva DGómez-Correa JBalderas-Mata S(2023)Cataractous eye fundus image enhancement by using multi-scale tone decompositionApplications of Digital Image Processing XLVI10.1117/12.2672348(40)Online publication date: 4-Oct-2023
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