# Supervised Deep Kriging for Single-Image Super-Resolution

## TL;DR

This paper introduces a supervised deep learning approach to single-image super-resolution based on kriging, enabling end-to-end training and statistical bias-variance estimation, achieving competitive results.

## Contribution

It combines kriging with deep learning to learn weights end-to-end, providing a novel statistical framework for super-resolution.

## Key findings

- Achieves competitive super-resolution performance.
- Enables bias and variance estimation in deep networks.
- Introduces a joint network for kriging-based super-resolution.

## Abstract

We propose a novel single-image super-resolution approach based on the geostatistical method of kriging. Kriging is a zero-bias minimum-variance estimator that performs spatial interpolation based on a weighted average of known observations. Rather than solving for the kriging weights via the traditional method of inverting covariance matrices, we propose a supervised form in which we learn a deep network to generate said weights. We combine the kriging weight generation and kriging process into a joint network that can be learned end-to-end. Our network achieves competitive super-resolution results as other state-of-the-art methods. In addition, since the super-resolution process follows a known statistical framework, we are able to estimate bias and variance, something which is rarely possible for other deep networks.