Open AccessBinary image enhancement based on aperiodic stochastic resonance
Author(s) -
Liu Jin,
Li Zan
Publication year - 2015
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.2014.0709
Subject(s) - aperiodic graph , computer science , binary image , binary number , artificial intelligence , image (mathematics) , noise (video) , peak signal to noise ratio , stochastic resonance , feature (linguistics) , signal to noise ratio (imaging) , computer vision , image enhancement , pattern recognition (psychology) , image processing , mathematics , telecommunications , linguistics , philosophy , arithmetic , combinatorics
The enhancement of noisy images has been playing a key role in improving the visual effect and the performance of image processing. Traditional methods for image enhancement are mainly focusing on eliminating noise, which cannot acquire good effect under low peak‐signal‐to‐noise ratio (PSNR) conditions. Stochastic resonance (SR), on the contrary, is a technique using noise to enhance signal. Owing to the unique feature of SR, a novel binary image enhancement scheme based on aperiodic SR (ASR) technique is proposed. In this study, the authors take the improvement in PSNR as a measure of the ASR‐based binary image enhancement system, which provides a guideline for the realisation of the ASR system. On this basis, they obtain the PSNR expression of the ASR‐based binary image enhancement system. Simulation results show that the proposed method is superior to the traditional binary image enhancement methods both in visual effect and PSNR performance.