Denoising of degraded facial image sequences based PCA
Fella Berrimi, Kara-Mohammed Chafia, Riadh Hedli · New Trends in Mathematical Science · 2021
One of the most stringent issues in image sequence processing lies in the high dimensionality of the image sequences.This issue is further made worse by the high correlation between the frames, on the one hand, and by the difficulty of noisy motion estimation, on the other hand.As a result, the de-noising algorithms are complex and require intense computational workload.To address this issue, we propose a novel algorithm based on the Principal Components Analysis (PCA) for dimension reduction, followed by a spatiotemporal filter for de-noising the dimensionally-reduced frames.The de-noising process is performed in the image reduced space by applying a temporal filter for motion compensation combined with a weighted average filter, and an wiener filter as a spatial filter for noise removal.The performance of the proposed method is proven using the Cohen-Kanade facial expressions (CKFE) database tested against different noise levels with PSNR and SSIM as metrics for objective evaluation.The experiments show that the proposed method gives better results than state of the art classical de-noising competitors.