Learning to predict super resolution wavelet coefficients

We develop a wavelet domain learning based technique for single image super resolution (SISR). First, we learn a mapping between a patch of approximate coefficients (ACs) and the detail coefficients (DCs) corresponding the center location of the patch using Neural Networks. We then obtain an SR image by using an approximate version of the original image (scaled as per the DWT size requirements of the final image) as ACs and by predicting the corresponding DCs using the mapping thus learnt. Our results compare favorably to both mature techniques and state of the art other learning based techniques.