# Single image super-resolution using self-optimizing mask via fractional-order gradient interpolation and reconstruction

## TL;DR

This paper introduces a novel single image super-resolution technique using adaptive fractional-order gradient interpolation, which enhances texture detail and structural preservation in high-resolution images from low-resolution inputs.

## Contribution

It proposes an innovative method combining fractional-order gradient interpolation with energy-based reconstruction, improving detail recovery and structural fidelity in super-resolution tasks.

## Key findings

- Outperforms existing super-resolution methods in quality metrics
- Preserves texture details effectively under large zoom levels
- Maintains structural similarity in reconstructed high-resolution images

## Abstract

Image super-resolution using self-optimizing mask via fractional-order gradient interpolation and reconstruction aims to recover detailed information from low-resolution images and reconstruct them into high-resolution images. Due to the limited amount of data and information retrieved from low-resolution images, it is difficult to restore clear, artifact-free images, while still preserving enough structure of the image such as the texture. This paper presents a new single image super-resolution method which is based on adaptive fractional-order gradient interpolation and reconstruction. The interpolated image gradient via optimal fractional-order gradient is first constructed according to the image similarity and afterwards the minimum energy function is employed to reconstruct the final high-resolution image. Fractional-order gradient based interpolation methods provide an additional degree of freedom which helps optimize the implementation quality due to the fact that an extra free parameter $\alpha$-order is being used. The proposed method is able to produce a rich texture detail while still being able to maintain structural similarity even under large zoom conditions. Experimental results show that the proposed method performs better than current single image super-resolution techniques.