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Most recent learning-based single-image super-resolution methods first interpolate the low-resolution (LR) input, from which overlapped LR features are then extracted to reconstruct their high-resolution (HR) counterparts and the final HR image. However, most of them neglect to take advantage of the intermediate recovered HR image to enhance image quality further. We conduct principal component analysis (PCA) to reduce LR feature dimension. Then we find that the number of principal components after conducting PCA in the LR feature space from the reconstructed images is larger than that from the interpolated images by using bicubic interpolation. Based on this observation, we present an unsophisticated yet effective framework named collaborative representation cascade (CRC) that learns multilayer mapping models between LR and HR feature pairs. In particular, we extract the features from the …