This paper is the first attempt to shed some light on this problem when the SR algorithms are designed for general natural images.
Under our theoretical framework, we could estimate the limits of learning-based SR algorithms.
We present a novel approach to reconstruction based super-resolution that explicitly models the detector's pixel layout and a new variant of the well known error back-projection super-resolution algorithm that makes use of the exact detector model in its back projection operator for better accuracy.
In this paper, we show that, when the perturbation theorem is invalid, the probability of successful superresolution is very low
In this paper, we focus on a major class of superresolution algorithms, called the reconstruction-based algorithms, which compute high-resolution images by simulating the image formation process.