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.INDEX TERMS Decolorization, grey image, image conversion, SVD, technological development.
I. INTRODUCTIONCurrently, most of the captured images are color images. However, it is frequently necessary to convert a color image to grayscale images for printing, aesthetic intents, object detection, and publishing as less expensive, helping colorblind people preserve visual cues [1]. Color-to-grayscale conversion is the process of reducing the image dimensions by transforming the RGB tristimulus values (Red, Green, Blue)
∈ R 3 to the intensity value (I) R [2]. The lightness valuesThe associate editor coordinating the review of this manuscript and approving it for publication was Mingbo Zhao . ranged from zero (black) to 255 (white), as shown in Fig. 1. Generally, the conversion process produces an image with lower contrast than the original color image. The goal of each conversion algorithm is to preserve the local luminance consistency, global consistency, image contrast information, and hue order as much as possible [3]. Different processing methods are required for each pixel in various image-processing applications. Processing RGB pixels is not feasible due to high storage requirements and computation costs. The best solution to these issues is to convert an RGB image into a grayscale image [4]. Grayscale images are mainly used for shape characteristics, edge detection, circular objects, and
