A Fractional Order Variational Framework for Retinex Fractional Order Partial Differential Equation Based Formulation for Multi Scale Nonlocal Contrast Enhancement with Texture Preserving - 2018

PROJECT TITLE :

A Fractional Order Variational Framework for Retinex Fractional Order Partial Differential Equation Based Formulation for Multi Scale Nonlocal Contrast Enhancement with Texture Preserving - 2018

ABSTRACT:

This Project discusses a novel conceptual formulation of the fractional-order variational framework for retinex, that could be a fractional-order partial differential equation (FPDE) formulation of retinex for the multi-scale nonlocal distinction enhancement with texture preserving. The well-known shortcomings of ancient integer-order computation-primarily based distinction-enhancement algorithms, like ringing artefacts and staircase effects, are still in nice want of special analysis attention. Fractional calculus has doubtless received prominence in applications within 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 flexibility of a fractional differential to boost the complex textural details of an image in an exceedingly nonlinear manner. Thus, in an try to deal with the aforementioned issues associated with traditional integer-order computation-based mostly distinction-enhancement algorithms, we have studied here, as an attention-grabbing theoretical problem, whether it will be possible to hybridize the capabilities of preserving the perimeters and the textural details of fractional calculus with texture image multi-scale nonlocal distinction enhancement. Motivated by this need, in this Project, we tend to introduce a completely unique 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 have a tendency to discuss the implementation of the restrictive fractional-order optimization algorithm and therefore the fractional-order Courant-Friedrichs-Lewy condition. Third, we tend to perform experiments to research the capability of the FPDE to preserve edges and textural details, whereas enhancing the contrast. The aptitude of the FPDE to preserve edges and textural details may be a elementary vital advantage, that makes our proposed algorithm superior to the ancient integer-order computation-based contrast enhancement algorithms, especially for images made in textural details.

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