Chebyshev and Conjugate Gradient Filters for Graph Image Denoising

Abstract

In 3D image/video acquisition, different views are often captured with varying noise levels across the views. In this paper, we propose a graph-based image enhancement technique that uses a higher quality view to enhance a degraded view. A depth map is utilized as auxiliary information to match the perspectives of the two views. Our method performs graph-based filtering of the noisy image by directly computing a projection of the image to be filtered onto a lower dimensional Krylov subspace of the graph Laplacian. We discuss two graph spectral denoising methods: first using Chebyshev polynomials, and second using iterations of the conjugate gradient algorithm. Our framework generalizes previously known polynomial graph filters, and we demonstrate through numerical simulations that our proposed technique produces subjectively cleaner images with about 1-3 dB improvement in PSNR over existing polynomial graph filters.

6 pages, 6 figures, accepted to 2014 IEEE International Conference on Multimedia and Expo Workshops (ICMEW)

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• Graph Signal Processing for Geometric Data and Beyond: Theory and Applications
• Edge-enhancing Filters with Negative Weights
• Accelerated graph-based nonlinear denoising filters
• Accelerated graph-based spectral polynomial filters
• Conjugate Gradient Acceleration of Non-Linear Smoothing Filters