CNN-based Rescaling Factor Estimation

We demonstrate the estimation of image resampling parameters in a deep learning framework by regressing the rescaling factor of 64x64-sized patches on features learned directly from grayscale intensities by a convolutional neural network (CNN). Our end-to-end network design comprises a simple concatenation of 25 convolutional layers with small 3 x 3 receptive fields and largely abstains from the use of pooling. We report experimental results on a large set of rescaled patches, for which the proposed CNN outperforms state-of-the-art frequency-domain estimators particularly in the case of downscaling. A critical discussion of sensitivities to mismatch between training and testing data points to failure cases.