Hand Depth Image Denoising and Superresolution via Noise-Aware Dictionaries

Hand Depth Image Denoising and Superresolution via Noise-Aware Dictionaries

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This paper proposes a two-stage method for hand depth image denoising and superresolution, using bilateral filters and learned dictionaries via noise-aware orthogonal matching pursuit read more (NAOMP) based K-SVD.The bilateral filtering phase recovers singular points and removes artifacts on silhouettes by averaging depth data using neighborhood pixels on which both depth difference and RGB similarity restrictions are imposed.The dictionary learning phase uses NAOMP for training dictionaries which separates faithful depth from noisy data.Compared with traditional OMP, NAOMP adds a residual reduction step which effectively weakens the noise term within the residual during the argan oil pure purple residual decomposition in terms of atoms.Experimental results demonstrate that the bilateral phase and the NAOMP-based learning dictionaries phase corporately denoise both virtual and real depth images effectively.