Wavelet-based depth map estimation for light field cameras

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Conventional depth estimation methods for light field cameras often fail to estimate sharp object boundaries. We propose a depth estimation method using a wavelet-based matching cost calculation and an estimation optimization, which can estimate sharp object boundaries. Our method first calculates matching costs on four subband images derived with a wavelet transform, and then combines the costs based on matching confidence. After an initial depth estimation by using the matching cost, we improve estimation by an optimization process. Experimental results demonstrated that our proposed method provides more plausible estimation with sharper object boundaries.

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