Fuzzified Contrast Enhancement for Nearly Invisible Images

Image enhancement is a basic requirement for any computer vision application for further processing of an image. A common limitation with most of the existing methods, when applied to nearly invisible images, is the loss of color details during the enhancement process. So, a fuzzy c-means clustering...

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Bibliographic Details
Published in:IEEE transactions on circuits and systems for video technology Vol. 32; no. 5; pp. 2802 - 2813
Main Authors: Kumar, Reman, Bhandari, Ashish Kumar
Format: Journal Article
Language:English
Published: New York IEEE 01.05.2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Algorithms
and nearly invisible images
Brightness
Clustering
Clustering algorithms
Color
Computer vision
Contrast enhancement
Discrete wavelet transforms
fuzzy c-means clustering
fuzzy clustering
histogram equalization
Histograms
Image color analysis
Image contrast
Image enhancement
Shape
ISSN:1051-8215, 1558-2205
Online Access:Get full text
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Summary:Image enhancement is a basic requirement for any computer vision application for further processing of an image. A common limitation with most of the existing methods, when applied to nearly invisible images, is the loss of color details during the enhancement process. So, a fuzzy c-means clustering-based method for image enhancement is proposed which enhances the perceptually invisible image along with preserving its color and naturalness. In this method, the image pixels are grouped into different clusters and are assigned membership values to those clusters. Based on this membership value, its intensity level is modified in the spatial domain. Modification of the gray levels proportional to the membership values leads to the stretching of the image histogram, similar in shape, to the original histogram. The process results in a very small shift in the mean intensity which preserves the color and brightness-related information of the image. The method enhances the image contrast and maintains the naturalness without introducing any artifacts. The simulation results on standard datasets reflect that the proposed algorithm is superior to many state-of-the-art and traditional methods for perceptually invisible images.
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ISSN:1051-8215
1558-2205
DOI:10.1109/TCSVT.2021.3098763