color image denoising using a hybrid algorithm based on singular spectrum analysis and principal component analysis methods

In this paper, a hybrid algorithm based on singular spectrum analysis and principal component analysis is proposed for denoising color images. the main novelty of this approach lies in the simultaneous utilization of singular spectrum analysis's capability to separate signal and noise in the time-series domain, along with principal component analysis's ability to remove correlations among the red, green, and blue channels of color images. to validate the effectiveness of the proposed method, peak signal-to-noise ratio and structural similarity index are employed on reference images that are contaminated with random noise at different levels. the experimental results indicate that the proposed algorithm achieves superior performance, particularly at higher noise levels. specifically, the results demonstrate higher peak signal-to-noise ratio and structural similarity values when compared with principal component analysis-based bootstrapping methods and eigenvalue-based denoising approaches.