Explorable Super Resolution
This addresses the need for interactive and diverse super-resolution outputs in fields like medical imaging, forensics, and graphics, though it is incremental as it builds on existing SR networks.
The paper tackles the problem that existing single image super-resolution methods do not allow exploring multiple plausible high-resolution reconstructions from a low-resolution input, and introduces a framework with a GUI and neural network backend that enables editing outputs to explore these possibilities while guaranteeing consistency with the input.
Single image super resolution (SR) has seen major performance leaps in recent years. However, existing methods do not allow exploring the infinitely many plausible reconstructions that might have given rise to the observed low-resolution (LR) image. These different explanations to the LR image may dramatically vary in their textures and fine details, and may often encode completely different semantic information. In this paper, we introduce the task of explorable super resolution. We propose a framework comprising a graphical user interface with a neural network backend, allowing editing the SR output so as to explore the abundance of plausible HR explanations to the LR input. At the heart of our method is a novel module that can wrap any existing SR network, analytically guaranteeing that its SR outputs would precisely match the LR input, when downsampled. Besides its importance in our setting, this module is guaranteed to decrease the reconstruction error of any SR network it wraps, and can be used to cope with blur kernels that are different from the one the network was trained for. We illustrate our approach in a variety of use cases, ranging from medical imaging and forensics, to graphics.