research-article

Detail and color enhancement in photo stylization

Authors:

David MouldAuthors Info & Claims

CAE '17: Proceedings of the symposium on Computational Aesthetics

Article No.: 5, Pages 1 - 11

https://doi.org/10.1145/3092912.3092917

Published: 29 July 2017 Publication History

Abstract

Abstraction in non-photorealistic rendering reduces the amount of detail, yet non-essential details can improve visual interest and thus make an image more appealing. In this paper, we propose an automatic system for photo manipulation that brightens an image and alters the detail levels. The process first applies an edge-preserving abstraction process to an input image, then uses the residual to reintroduce and exaggerate details in areas near strong edges. At the same time, image regions further from strong edges are brightened. The final result is a lively mixture of abstraction and enhanced detail.

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Cited By

Al-Ameen Z(2023)Efficient image color enhancement using a new tint intensification algorithmJournal of Real-Time Image Processing10.1007/s11554-023-01334-320:4Online publication date: 2-Jul-2023
https://dl.acm.org/doi/10.1007/s11554-023-01334-3
Rosin PLai YMould DYi RBerger IDoyle LLee SLi CLiu YSemmo AShamir ASon MWinnemöller H(2022)NPRportrait 1.0: A three-level benchmark for non-photorealistic rendering of portraitsComputational Visual Media10.1007/s41095-021-0255-38:3(445-465)Online publication date: 6-Apr-2022
https://doi.org/10.1007/s41095-021-0255-3
Pavan Kumar MPoornima BNagendraswamy HManjunath CRangaswamy BVarsha MVinutha H(2021)Image Abstraction Framework as a Pre-processing Technique for Accurate Classification of Archaeological Monuments Using Machine Learning ApproachesSN Computer Science10.1007/s42979-021-00935-83:1Online publication date: 26-Nov-2021
https://doi.org/10.1007/s42979-021-00935-8
Show More Cited By

Index Terms

Detail and color enhancement in photo stylization
1. Computing methodologies
  1. Computer graphics
    1. Rendering
      1. Non-photorealistic rendering

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Published In

cover image ACM Conferences

CAE '17: Proceedings of the symposium on Computational Aesthetics

July 2017

71 pages

ISBN:9781450350808

DOI:10.1145/3092912

Conference Chairs:
Holger Winnemoeller
Adobe Systems, Inc.
,
Lyn Bartram
Simon Fraser University, Canada
,
Editor:
Stephen N. Spencer
University of Washington
,
General Chairs:
Bruce Gooch
Texas A&M University
,
Yotam Gingold
George Mason University

Copyright © 2017 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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SIGGRAPH: ACM Special Interest Group on Computer Graphics and Interactive Techniques
EUROGRAPHICS: The European Association for Computer Graphics

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 29 July 2017

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Expressive '17

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SIGGRAPH
EUROGRAPHICS

Expressive '17: The joint symposium on Computational Aesthetics and Sketch Based Interfaces and Modeling and Non-Photorealistic Animation and Rendering

July 29 - 30, 2017

California, Los Angeles

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Overall Acceptance Rate 11 of 25 submissions, 44%

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Cited By

Al-Ameen Z(2023)Efficient image color enhancement using a new tint intensification algorithmJournal of Real-Time Image Processing10.1007/s11554-023-01334-320:4Online publication date: 2-Jul-2023
https://dl.acm.org/doi/10.1007/s11554-023-01334-3
Rosin PLai YMould DYi RBerger IDoyle LLee SLi CLiu YSemmo AShamir ASon MWinnemöller H(2022)NPRportrait 1.0: A three-level benchmark for non-photorealistic rendering of portraitsComputational Visual Media10.1007/s41095-021-0255-38:3(445-465)Online publication date: 6-Apr-2022
https://doi.org/10.1007/s41095-021-0255-3
Pavan Kumar MPoornima BNagendraswamy HManjunath CRangaswamy BVarsha MVinutha H(2021)Image Abstraction Framework as a Pre-processing Technique for Accurate Classification of Archaeological Monuments Using Machine Learning ApproachesSN Computer Science10.1007/s42979-021-00935-83:1Online publication date: 26-Nov-2021
https://doi.org/10.1007/s42979-021-00935-8
Kumar MPoornima BNagendraswamy HManjunath C(2021)Structure-preserving NPR framework for image abstraction and stylizationThe Journal of Supercomputing10.1007/s11227-020-03547-wOnline publication date: 21-Jan-2021
https://doi.org/10.1007/s11227-020-03547-w
Kumar MPoornima BNagendraswamy HManjunath C(2019)A comprehensive survey on non-photorealistic rendering and benchmark developments for image abstraction and stylizationIran Journal of Computer Science10.1007/s42044-019-00034-12:3(131-165)Online publication date: 3-May-2019
https://doi.org/10.1007/s42044-019-00034-1

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