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We present a learning-based single image super-resolution (SISR) method to obtain a high resolution (HR) image from a single given low resolution (LR) image. Our method gives more accurate results while also testing (runs) and training faster with a smaller number of training samples compared to other methods. We posed SISR as a problem of estimating a function to predict the pixels of an HR patch...
We formulate the problem of single image super resolution (SR) in terms of learning a single but general nonlinear function. This function takes a low resolution (LR) image patch input and predicts the high resolution (HR) image pixels corresponding to the center pixel of the patch. For training, we use a LR version of an input image, and the given image pixels as target, thus obviating the need for...
In this paper a novel learning based technique for single image super resolution (SR) is proposed. We model the relationship between available low resolution (LR) image and desired high resolution (HR) image as multi-scale markov random field (MSMRF). We re-formulate the SR problem in terms of learning the mapping between LR-MRF and HR-MRF, which is generally non-linear. Instead of learning MSMRF...
We move closer to deriving an image-driven criteria for the choice of wavelets for single image super resolution (SR). We start with the hypothesis that higher edge densities are better reconstructed by wavelets with higher number of vanishing moments and smaller support size. We examine SR performance of different wavelets on image categories with different amount of details. We use the slope of...
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...
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