Fast single image super-resolution based on sigmoid transformation

TL;DR

This paper introduces a fast, simple super-resolution method using patch-wise sigmoid transformation as a regularization term, achieving superior reconstruction quality efficiently.

Contribution

It proposes a novel super-resolution approach that employs sigmoid transformation for regularization, enhancing performance and speed over existing methods.

Findings

Outperforms state-of-the-art super-resolution methods in quality.

Demonstrates high efficiency and fast processing.

Provides effective image detail reconstruction.

Abstract

Single image super-resolution aims to generate a high-resolution image from a single low-resolution image, which is of great significance in extensive applications. As an ill-posed problem, numerous methods have been proposed to reconstruct the missing image details based on exemplars or priors. In this paper, we propose a fast and simple single image super-resolution strategy utilizing patch-wise sigmoid transformation as an imposed sharpening regularization term in the reconstruction, which realizes amazing reconstruction performance. Extensive experiments compared with other state-of-the-art approaches demonstrate the superior effectiveness and efficiency of the proposed algorithm.