A regret-based Pareto optimization jointly maximizes contrast map variance for event alignment and minimizes it for denoising, yielding better results than separate processing in experiments on denoising and motion estimation.
Neuromorphic Imaging with Density-based Spatiotemporal Denoising,
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A spatiotemporal graph is built from raw events; its Laplacian eigenvectors, computed via a reordered matrix using an event-density prior, are used to filter noise events.
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Joint Alignment and Denoising for Event-Based Vision Sensors Using Regret-based Pareto Optimization
A regret-based Pareto optimization jointly maximizes contrast map variance for event alignment and minimizes it for denoising, yielding better results than separate processing in experiments on denoising and motion estimation.
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Denoising for Neuromorphic Cameras Based on Graph Spectral Features
A spatiotemporal graph is built from raw events; its Laplacian eigenvectors, computed via a reordered matrix using an event-density prior, are used to filter noise events.