Domain-agnostic single-image super-resolution via a meta-transfer neural architecture search

•We developed a novel internal learning process that finds the network architecture and trains the weights for the test image and blur kernel.•By designing the training sample for external and internal learning, the knowledge from the external and internal data can be reflected, leading to fast convergence.•We present a fractal network as an elegant super-network for an element-wise architecture search to adapt to the pixels from the test image.•The proposed method represents a generalized SR algorithm for various domain images with different kernels , and this ability is demonstrated experimentally.