Open AccessNoise‐refined image enhancement using multi‐objective optimisation
Author(s) -
Peng Renbin,
Varshney Pramod K.
Publication year - 2013
Publication title -
iet image processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.401
H-Index - 45
eISSN - 1751-9667
pISSN - 1751-9659
DOI - 10.1049/iet-ipr.2011.0603
Subject(s) - noise (video) , image (mathematics) , image enhancement , image noise , genetic algorithm , image quality , computer science , similarity (geometry) , stochastic resonance , artificial intelligence , algorithm , mathematics , mathematical optimization , computer vision , pattern recognition (psychology)
This study presents a novel scheme for the enhancement of images using stochastic resonance (SR) noise. In this scheme, a suitable dose of noise is added to the lower quality images such that the performance of a sub‐optimal image enhancer is improved without altering its parameters. Image enhancement is modelled as a constrained multi‐objective optimisation (MOO) problem, with similarity and some desired image‐enhancement characteristic being the two objective functions. The principle of SR noise‐refined image enhancement is analysed, and an image‐enhancement system is developed. A genetic algorithm‐based MOO technique is employed to find the optimum parameters of the SR noise distribution. Several image‐enhancement examples are provided, where the efficiency of the presented method in several real‐world applications is shown.