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An adaptive speckle denoising method called Volume-BM3D is developed for optical coherence tomography (OCT) volume data processing, including an adaptive noise level estimation algorithm and a denoising strategy based on block-matching and filtering in a highly sparse local 3-D transform domain. Noise level estimation, an important step for denoising, is obtained from estimating signal fluctuations among neighboring A-Scans. Unlike 2-D denoising approaches, the Volume-BM3D considers the similarities of tissue structures either in a single 2-D image or in a volume data simultaneously. It not only effectively suppresses speckle noise, but also improves the visualization of small morphological features, such as sweat glands in finger images. Application of this proposed method to the OCT volume data of a human finger tip shows an 18.4-dB signal-to-noise ratio improvement in speckle noise reduction with little edge blurring. It outperforms other filtering methods in suppressing speckle noises and revealing attenuated subtle features.