research-article

Parameterized Logarithmic Framework for Image Enhancement

Authors:

K. Panetta,

S. Agaian,

Yicong Zhou,

E. J. WhartonAuthors Info & Claims

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, Volume 41, Issue 2

Pages 460 - 473

https://doi.org/10.1109/TSMCB.2010.2058847

Published: 01 April 2011 Publication History

Abstract

Image processing technologies such as image enhancement generally utilize linear arithmetic operations to manipulate images. Recently, Jourlin and Pinoli successfully used the logarithmic image processing (LIP) model for several applications of image processing such as image enhancement and segmentation. In this paper, we introduce a parameterized LIP (PLIP) model that spans both the linear arithmetic and LIP operations and all scenarios in between within a single unified model. We also introduce both frequency- and spatial-domain PLIP-based image enhancement methods, including the PLIP Lee's algorithm, PLIP bihistogram equalization, and the PLIP alpha rooting. Computer simulations and comparisons demonstrate that the new PLIP model allows the user to obtain improved enhancement performance by changing only the PLIP parameters, to yield better image fusion results by utilizing the PLIP addition or image multiplication, to represent a larger span of cases than the LIP and linear arithmetic cases by changing parameters, and to utilize and illustrate the logarithmic exponential operation for image fusion and enhancement.

Cited By

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Li XXu HJiang GYu MLuo TZhang XYing H(2023)Underwater Image Quality Assessment from Synthetic to Real-world: Dataset and Objective MethodACM Transactions on Multimedia Computing, Communications, and Applications10.1145/362498320:3(1-23)Online publication date: 23-Oct-2023
https://dl.acm.org/doi/10.1145/3624983
Guo PLiu HZeng DXiang TLi LGu K(2023)An Underwater Image Quality Assessment MetricIEEE Transactions on Multimedia10.1109/TMM.2022.318721225(5093-5106)Online publication date: 1-Jan-2023
https://dl.acm.org/doi/10.1109/TMM.2022.3187212
Lepcha DGoyal BDogra ASharma KGupta D(2023)A deep journey into image enhancementInformation Fusion10.1016/j.inffus.2022.12.01293:C(36-76)Online publication date: 1-May-2023
https://dl.acm.org/doi/10.1016/j.inffus.2022.12.012
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Parameterized Logarithmic Framework for Image Enhancement
1. Computing methodologies
  1. Artificial intelligence
  2. Computer graphics

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cover image IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics Volume 41, Issue 2

April 2011

289 pages

ISSN:1083-4419

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IEEE Press

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Published: 01 April 2011

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Li XXu HJiang GYu MLuo TZhang XYing H(2023)Underwater Image Quality Assessment from Synthetic to Real-world: Dataset and Objective MethodACM Transactions on Multimedia Computing, Communications, and Applications10.1145/362498320:3(1-23)Online publication date: 23-Oct-2023
https://dl.acm.org/doi/10.1145/3624983
Guo PLiu HZeng DXiang TLi LGu K(2023)An Underwater Image Quality Assessment MetricIEEE Transactions on Multimedia10.1109/TMM.2022.318721225(5093-5106)Online publication date: 1-Jan-2023
https://dl.acm.org/doi/10.1109/TMM.2022.3187212
Lepcha DGoyal BDogra ASharma KGupta D(2023)A deep journey into image enhancementInformation Fusion10.1016/j.inffus.2022.12.01293:C(36-76)Online publication date: 1-May-2023
https://dl.acm.org/doi/10.1016/j.inffus.2022.12.012
Choukali MValizadeh MAmirani MMirbolouk S(2023)A desired histogram estimation accompanied with an exact histogram matching method for image contrast enhancementMultimedia Tools and Applications10.1007/s11042-023-14830-282:18(28345-28365)Online publication date: 1-Jul-2023
https://dl.acm.org/doi/10.1007/s11042-023-14830-2
He LLong WLiu SLi YDing W(2023)A new grey mapping function and its adaptive algorithm for low-light image enhancementMultimedia Tools and Applications10.1007/s11042-022-13598-182:4(6071-6096)Online publication date: 1-Feb-2023
https://dl.acm.org/doi/10.1007/s11042-022-13598-1
Bhandari ASingh NSingh A(2022)Swarm-based optimally selected histogram computation system for image enhancementNeural Computing and Applications10.1007/s00521-021-06858-y34:9(7053-7067)Online publication date: 1-May-2022
https://dl.acm.org/doi/10.1007/s00521-021-06858-y
Tang CJia RRen XCui YWang B(2022)Structure–texture decomposition-based dehazing of a single image with large sky areaMachine Vision and Applications10.1007/s00138-022-01321-x33:5Online publication date: 29-Jul-2022
https://dl.acm.org/doi/10.1007/s00138-022-01321-x
Mirbolouk SValizadeh MAmirani MChoukali M(2020)A fuzzy histogram weighting method for efficient image contrast enhancementMultimedia Tools and Applications10.1007/s11042-020-09801-w80:2(2221-2241)Online publication date: 12-Sep-2020
https://dl.acm.org/doi/10.1007/s11042-020-09801-w
Zhang LJia ZKoefoed LYang JKasabov N(2020)Remote sensing image enhancement based on the combination of adaptive nonlinear gain and the PLIP model in the NSST domainMultimedia Tools and Applications10.1007/s11042-019-08586-x79:19-20(13647-13665)Online publication date: 1-Feb-2020
https://dl.acm.org/doi/10.1007/s11042-019-08586-x
Choukali MValizadeh MChehel Amirani M(2019)An efficient contrast enhancement method using repulsive force of edgesMultidimensional Systems and Signal Processing10.1007/s11045-019-00666-331:1(299-315)Online publication date: 25-Jun-2019
https://dl.acm.org/doi/10.1007/s11045-019-00666-3
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