# Adaptive Low-Resolution Combination Search for Reference-Independent Image Super-Resolution

## TL;DR

This paper introduces a new image super-resolution method that reconstructs high-resolution images from low-resolution inputs without needing high-resolution references.

## Contribution

The novel contribution is an adaptive search algorithm that combines low-resolution images to reconstruct high-resolution content using a unified degradation model.

## Key findings

- The proposed method improves PSNR by 27.33% and SSIM by 44.64% on the USAF 1951 resolution target.
- In semiconductor chip inspection, PSNR increases by 22.36% and SSIM by 40.38%.

## Abstract

Accurately reconstructing high-resolution (HR) images remains challenging in scenarios where HR observations cannot be captured due to optical, hardware, or cost constraints. To address this limitation, we introduce an image super-resolution (SR) framework that reconstructs HR content solely from multiple low-resolution (LR) measurements, without relying on any HR reference images. The proposed method formulates a unified degradation model that describes how HR pixels contribute to LR observations under subpixel shifts and anisotropic downsampling. Based on this model, we develop an adaptive search algorithm capable of identifying the minimal and most informative combination of LR images required to equivalently represent the latent HR image. The selected LR images are then used to construct a solvable linear system whose solution directly yields the HR pixel values. Experiments conducted on the USAF 1951 resolution target demonstrate that the proposed approach improves Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity (SSIM) by 27.33% and 44.64%, respectively, achieving a resolvable spatial frequency of 228 line pairs per millimeter. In semiconductor chip inspection, PSNR and SSIM increase by 22.36% and 40.38%. These results verify that the proposed LR-combination-based strategy provides a physically interpretable and highly practical alternative for applications in which HR reference images cannot be obtained.