A Progressive Meta-Algorithm for Large and Seamless Super Resolution Images

We present a progressive meta-algorithm for single image super resolution of very large and seamless images. Deep single image super resolution backbone networks are capable of upsampling local image patches but are susceptible to severe tiling artifacts when attempting to integrate the patches together into a single large image. For large images such as 4K images, the individual low-resolution patches also comprise a narrow receptive field that lacks context regarding the pixel information from the surrounding patches. Our Progressive meta-algorithm is inspired by prior works in progressive GANs and is designed to resolve inter-patch tiling artifact across varying scales while further incorporating context by expanding the receptive field to include the surrounding patches through multiple rounds of progressive upsampling. Our meta-algorithm is compatible with standard super-resolving backbones and enables super-resolution of very large images such as 4K images while overcoming practical GPU memory limitations with commodity graphics cards. We evaluate our Meta-Algorithm with the challenging task of 16x super resolution for 4096×4096 images using the ESRGAN super-resolving backbone and demonstrate significantly improved image quality versus a baseline patch-based approach as evaluated using the Learned Perceptual Image Patch Similarity (LPIPS) metric.