Fast Learning-Based Single Image Super-Resolution

Neeraj Kumar, Amit Sethi

Abstract: 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 using its corresponding LR pixels and their spatial neighborhood. We studied the impact of varying the input LR and output HR patch sizes and gained the following insights: reconstruction accuracy for a given output HR patch size improves when input LR patch size is increased, but the improvement saturates after including a few extra layers of LR pixels. Moreover, HR reconstruction accuracy is the highest when the output HR patch is restricted to only that which corresponds to one LR pixel. We used zero component analysis as a pre-processing step to enhance the estimation optimization energy on perceptually salient features such as edges. We tapped into the ability of polynomial neural networks to hierarchically learn refinements of a function that maps LR to HR patches. Accurate HR reconstruction with small input and output patch sizes not only makes learning more efficient, it also indicates that SISR is a highly local problem. In contrast, a recently proposed and related technique using convolutional neural networks needs much larger training set and longer training time because of larger input-output patch sizes and a computationally expensive learning algorithm.