Color to Grayscale Image Conversion Based on Singular Value Decomposition

Color information is useless for distinguishing significant edges and features in numerous applications. In image processing, a gray image discards much-unrequired data in a color image. The primary drawback of colour-to-grey conversion is eliminating the visually significant image pixels. A current...

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Bibliographic Details
Published in:IEEE access 2023-01, Vol.11, p.1-1
Main Authors: Khudhair, Zaid N., Nidhal, Ahmed, El Abbadi, Nidhal K., Mohamed, Farhan, Saba, Tanzila, Alamri, Faten S., Rehman, Amjad
Format: Article
Language:English
Subjects:
Color
Color imagery
Colored noise
Conversion
Correlation
Decolorization
Decomposition
Distortion
Gray scale
grey image
Image color analysis
image conversion
Image processing
Image reconstruction
Pixels
Singular value decomposition
SVD
technological development
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Summary:Color information is useless for distinguishing significant edges and features in numerous applications. In image processing, a gray image discards much-unrequired data in a color image. The primary drawback of colour-to-grey conversion is eliminating the visually significant image pixels. A current proposal is a novel approach for transforming an RGB image into a grayscale image based on singular value decomposition (SVD). A specific factor magnifies one of the color channels (Red, Green, and Blue). A vector of three values (Red, Green, Blue) of each pixel in an image is decomposed using SVD into three matrices. The norm of the diagonal matrix was determined and then divided by a specific factor to obtain the grey value of the corresponding pixel. The contribution of the proposed method gives the user high flexibility to produce many versions of gray images with varying contrasts, which is very helpful in many applications. Furthermore, SVD allows for image reconstruction by combining the weighting of each channel with the singular value matrix. This results in a grayscale image that more accurately captures the actual intensity values of the image and preserves more color information than traditional grayscale conversion methods, resulting in loss of color information. The proposed method was compared with a similar method (converting the color image into grayscale) and was found to be the most efficient.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2023.3279734