A fusion of cuckoo search and multiscale adaptive smoothing based unsharp masking for image enhancement

Lalit Maurya, Prasant Kumar Mahapatra, Amod Kumar

Research output: Contribution to journalArticlepeer-review

Abstract

Image enhancement means to improve the visual appearance of an image by increasing its contrast and sharpening the features. This article presents a fusion of cuckoo search optimization-based image enhancement (CS-IE) and multiscale adaptive smoothing based unsharping method (MAS-UM) for image enhancement. The fusion strategy is introduced to improve the deficiency of enhanced image that suppresses the saturation and over-sharpness artefacts in order to obtain a visually pleasing result. The ideology behind the selection of fusion images (candidate) is that one image should have high sharpness or contrast with maximum entropy and other should be high Peak Signal-to-Noise Ratio (PSNR) sharp image, to provide a better trade-off between sharpness and noise. In this article, the CS-IE and MAS-UM results are fused to combine their complementary advantages. The proposed algorithms are applied to lathe tool images and some natural standard images to verify their effectiveness. The results are compared with conventional enhancement techniques such as Histogram equalization (HE), Linear contrast stretching (LCS), Contrast-limited adaptive histogram equalization (CLAHE), standard PSO image enhancement (PSO-IE), Differential evolution image enhancement (DE-IE) and Firefly algorithm-based image enhancement (FA-IE) techniques.

Original languageEnglish
Pages (from-to)151-174
Number of pages24
JournalInternational Journal of Applied Metaheuristic Computing
Volume10
Issue number3
DOIs
Publication statusPublished - 1 Jul 2019

Keywords

  • Cuckoo Search Algorithm
  • Histogram Equalization
  • Image Enhancement
  • Linear Contrast Stretching
  • Multiscale Adaptive Smoothing Technique
  • Particle Swarm Optimization Algorithm
  • Unsharp Masking

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