A Fractional-Order Variational Framework for Retinex : Fractional-Order Partial Differential Equation-Based Formulation for Multi-Scale Nonlocal Contrast Enhancement with Texture Preserving

This paper discusses a novel conceptual formulation of the fractional-order variational framework for retinex, which is a fractional-order partial differential equation (FPDE) formulation of retinex for the multi-scale nonlocal contrast enhancement with texture preserving. The well-known shortcoming...

Ausführliche Beschreibung

Bibliographische Detailangaben
Veröffentlicht in:IEEE transactions on image processing : a publication of the IEEE Signal Processing Society. - 1992. - 27(2018), 3 vom: 14. März, Seite 1214-1229
1. Verfasser: Yi-Fei Pu (VerfasserIn)
Weitere Verfasser: Siarry, Patrick, Chatterjee, Amitava, Zheng-Ning Wang, Zhang Yi, Yi-Guang Liu, Ji-Liu Zhou, Yan Wang
Format: Online-Aufsatz
Sprache:English
Veröffentlicht: 2018
Zugriff auf das übergeordnete Werk:IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
Schlagworte:Journal Article
LEADER 01000naa a22002652 4500
001 NLM286327848
003 DE-627
005 20231225051442.0
007 cr uuu---uuuuu
008 231225s2018 xx |||||o 00| ||eng c
024 7 |a 10.1109/TIP.2017.2779601  |2 doi 
028 5 2 |a pubmed24n0954.xml 
035 |a (DE-627)NLM286327848 
035 |a (NLM)29990194 
040 |a DE-627  |b ger  |c DE-627  |e rakwb 
041 |a eng 
100 1 |a Yi-Fei Pu  |e verfasserin  |4 aut 
245 1 2 |a A Fractional-Order Variational Framework for Retinex  |b Fractional-Order Partial Differential Equation-Based Formulation for Multi-Scale Nonlocal Contrast Enhancement with Texture Preserving 
264 1 |c 2018 
336 |a Text  |b txt  |2 rdacontent 
337 |a ƒaComputermedien  |b c  |2 rdamedia 
338 |a ƒa Online-Ressource  |b cr  |2 rdacarrier 
500 |a Date Completed 11.12.2018 
500 |a Date Revised 11.12.2018 
500 |a published: Print-Electronic 
500 |a Citation Status PubMed-not-MEDLINE 
520 |a This paper discusses a novel conceptual formulation of the fractional-order variational framework for retinex, which is a fractional-order partial differential equation (FPDE) formulation of retinex for the multi-scale nonlocal contrast enhancement with texture preserving. The well-known shortcomings of traditional integer-order computation-based contrast-enhancement algorithms, such as ringing artefacts and staircase effects, are still in great need of special research attention. Fractional calculus has potentially received prominence in applications in the domain of signal processing and image processing mainly because of its strengths like long-term memory, nonlocality, and weak singularity, and because of the ability of a fractional differential to enhance the complex textural details of an image in a nonlinear manner. Therefore, in an attempt to address the aforementioned problems associated with traditional integer-order computation-based contrast-enhancement algorithms, we have studied here, as an interesting theoretical problem, whether it will be possible to hybridize the capabilities of preserving the edges and the textural details of fractional calculus with texture image multi-scale nonlocal contrast enhancement. Motivated by this need, in this paper, we introduce a novel conceptual formulation of the fractional-order variational framework for retinex. First, we implement the FPDE by means of the fractional-order steepest descent method. Second, we discuss the implementation of the restrictive fractional-order optimization algorithm and the fractional-order Courant-Friedrichs-Lewy condition. Third, we perform experiments to analyze the capability of the FPDE to preserve edges and textural details, while enhancing the contrast. The capability of the FPDE to preserve edges and textural details is a fundamental important advantage, which makes our proposed algorithm superior to the traditional integer-order computation-based contrast enhancement algorithms, especially for images rich in textural details 
650 4 |a Journal Article 
700 1 |a Siarry, Patrick  |e verfasserin  |4 aut 
700 1 |a Chatterjee, Amitava  |e verfasserin  |4 aut 
700 1 |a Zheng-Ning Wang  |e verfasserin  |4 aut 
700 1 |a Zhang Yi  |e verfasserin  |4 aut 
700 1 |a Yi-Guang Liu  |e verfasserin  |4 aut 
700 1 |a Ji-Liu Zhou  |e verfasserin  |4 aut 
700 1 |a Yan Wang  |e verfasserin  |4 aut 
773 0 8 |i Enthalten in  |t IEEE transactions on image processing : a publication of the IEEE Signal Processing Society  |d 1992  |g 27(2018), 3 vom: 14. März, Seite 1214-1229  |w (DE-627)NLM09821456X  |x 1941-0042  |7 nnns 
773 1 8 |g volume:27  |g year:2018  |g number:3  |g day:14  |g month:03  |g pages:1214-1229 
856 4 0 |u http://dx.doi.org/10.1109/TIP.2017.2779601  |3 Volltext 
912 |a GBV_USEFLAG_A 
912 |a SYSFLAG_A 
912 |a GBV_NLM 
912 |a GBV_ILN_350 
951 |a AR 
952 |d 27  |j 2018  |e 3  |b 14  |c 03  |h 1214-1229