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Deep interpolation – denoising data by removing independent noise

Deep Interpolation deepinterpolation is a Python library to denoise data by removing independent noise. Importantly training does NOT require ground truth. Principle of Deep Interpolation Figure 1 – Schematic introducing the principles of deep interpolation. A. An interpolation model is trained to predict a noisy block from other blocks with independent noise. The loss is…

Deep Interpolation

deepinterpolation is a Python library to denoise data by removing independent noise. Importantly training does NOT require ground truth.

Principle of Deep Interpolation

principle of deep interpolation

Figure 1 – Schematic introducing the principles of deep interpolation. A. An interpolation model is trained to predict a noisy block from other blocks with independent noise. The loss is the difference between the predicted data and a new noisy block. B. The interpolation model is used to create a noiseless version of the input data.

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