Artificial bee Colony optimized image enhancement framework for invisible images

Image enhancement plays an important role in image processing to obtain an image with more perceptual details. In this paper, an artificial bee colony optimization based weighted gamma correction method is proposed to improve the visual quality of the contrast distorted images. The proposed method i...

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Veröffentlicht in:Multimedia tools and applications 2023, Vol.82 (3), p.3627-3646
Hauptverfasser: Veluchamy, Magudeeswaran, Subramani, Bharath
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Subramani, Bharath
description Image enhancement plays an important role in image processing to obtain an image with more perceptual details. In this paper, an artificial bee colony optimization based weighted gamma correction method is proposed to improve the visual quality of the contrast distorted images. The proposed method improves the perceived contrast by expanding and compressing the pixel values. First, Image Expansion and Compression are employed to expose and confine the intensity level present in the image, respectively. Then, an optimally weighted sum approach is used to increase the essential details in the dark regions. Finally, an artificial bee colony optimization algorithm is employed to compute the optimal weighting parameter for brightness preservation. Experimental results demonstrate that the proposed method yields better visual quality images and highlights fine details by enhancing contrast and brightness. The proposed method’s quantitative results are competitive compared to the other well-known methods.
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source Springer Nature - Complete Springer Journals
subjects Algorithms
Brightness
Computer Communication Networks
Computer Science
Data Structures and Information Theory
Image compression
Image contrast
Image enhancement
Image processing
Image quality
Methods
Multimedia
Multimedia Information Systems
Optimization
Optimization algorithms
Process controls
Special Purpose and Application-Based Systems
Swarm intelligence
title Artificial bee Colony optimized image enhancement framework for invisible images
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