Open AccessImproved sub‐band adaptive thresholding function for denoising of satellite image based on evolutionary algorithms
Author(s) -
Soni Vivek,
Bhandari Ashish Kumar,
Kumar Anil,
Singh Girish Kumar
Publication year - 2013
Publication title -
iet signal processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.384
H-Index - 42
ISSN - 1751-9683
DOI - 10.1049/iet-spr.2013.0139
Subject(s) - cuckoo search , noise reduction , thresholding , artificial intelligence , computer science , particle swarm optimization , peak signal to noise ratio , pattern recognition (psychology) , algorithm , video denoising , noise (video) , image (mathematics) , video tracking , object (grammar) , multiview video coding
In this study, an improved method based on evolutionary algorithms for denoising of satellite images is proposed. In this approach, the stochastic global optimisation techniques such as Cuckoo Search (CS) algorithm, artificial bee colony (ABC), and particle swarm optimisation (PSO) technique and their different variants are exploited for learning the parameters of adaptive thresholding function required for optimum performance. It was found that the CS algorithm and ABC algorithm‐based denoising approach give better performance in terms of edge preservation index or edge keeping index (EPI or EKI) peak signal‐to‐noise ratio (PSNR) and signal‐to‐noise ratio (SNR) as compared to PSO‐based denoising approach. The proposed technique has been tested on satellite images. The quantitative (EPI, PSNR and SNR) and visual (denoised images) results show superiority of the proposed technique over conventional and state‐of‐the‐art image denoising techniques.