Training-Free, Single-Image Super-Resolution Using a Dynamic Convolutional Network

The typical approach for solving the problem of single-image super-resolution (SR) is to learn a nonlinear mapping between the low-resolution (LR) and high-resolution (HR) representations of images in a training set. Training-based approaches can be tuned to give high accuracy on a given class of images, but they call for retraining if the HR <inline-formula><tex-math notation="LaTeX">$\rightarrow$</tex-math></inline-formula> LR generative model deviates or if the test images belong to a different class, which limits their applicability. On the other hand, we propose a solution that does not require a training dataset. Our method relies on constructing a dynamic convolutional network (DCN) to learn the relation between the consecutive scales of Gaussian and Laplacian pyramids. The relation is in turn used to predict the detail at a finer scale, thus leading to SR. Comparisons with state-of-the-art techniques on standard datasets show that the proposed DCN approach results in about 0.8 and 0.3 dB gain in peak signal-to-noise ratio for <inline-formula><tex-math notation="LaTeX">$2\times$</tex-math></inline-formula> and <inline-formula><tex-math notation="LaTeX">$3\times$</tex-math></inline-formula> SR, respectively. The structural similarity index is on par with the competing techniques.