Abstract:

The RGB color ring is known as the most understandable color representation in human vision, as it has complementary colors. However, color relationships hardly ever play a function in wavelet-primarily based totally color image processing tools. In this study, Complementary Color Wavelet Transform (CCWT), which is supported by complementary color relationships and complex wavelet design techniques, is used to denoise in color images. This wavelet consists of a family of two-dimensional complex wavelets with a phase difference of 2π/3 obtained from the angle relationship between the color axes of the RGB color ring, and is very effective in terms of directional selectivity. By using the coefficients of the directions in different phases, denoising processes are performed from the multi-channel color images. It was validated the performance of CCWT using various color images and noise levels, based on peak signal-to-noise ratio, structural similarity index, mean square error values, and visual quality. CCWT was compared with state-of-the-art multi-resolution image denoising algorithms, and found that the method achieves superior denoising performance both quantitatively and visually. It was also analyzed the computation time of CCWT and compared it with existing approaches.

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