Next Article in Journal
Sensors Based on Optical and Photonic Devices
Previous Article in Journal
A PI-Dual-STGCN Fault Diagnosis Model Based on the SHAP-LLM Joint Explanation Framework
 
 
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
This is an early access version, the complete PDF, HTML, and XML versions will be available soon.
Article

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

Key Laboratory of High-Efficiency and Clean Mechanical Manufacture (Ministry of Education), National Experimental Teaching Demonstration Center for Mechanical Engineering (Shandong University), School of Mechanical Engineering, Shandong University, Jinan 250061, China
Sensors 2026, 26(2), 725; https://doi.org/10.3390/s26020725
Submission received: 11 December 2025 / Revised: 17 January 2026 / Accepted: 19 January 2026 / Published: 21 January 2026
(This article belongs to the Section Sensing and Imaging)

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.
Keywords: image super-resolution; reference-free reconstruction; adaptive search algorithm; subpixel shift; degradation modeling image super-resolution; reference-free reconstruction; adaptive search algorithm; subpixel shift; degradation modeling

Share and Cite

MDPI and ACS Style

Tian, Y. Adaptive Low-Resolution Combination Search for Reference-Independent Image Super-Resolution. Sensors 2026, 26, 725. https://doi.org/10.3390/s26020725

AMA Style

Tian Y. Adaptive Low-Resolution Combination Search for Reference-Independent Image Super-Resolution. Sensors. 2026; 26(2):725. https://doi.org/10.3390/s26020725

Chicago/Turabian Style

Tian, Ye. 2026. "Adaptive Low-Resolution Combination Search for Reference-Independent Image Super-Resolution" Sensors 26, no. 2: 725. https://doi.org/10.3390/s26020725

APA Style

Tian, Y. (2026). Adaptive Low-Resolution Combination Search for Reference-Independent Image Super-Resolution. Sensors, 26(2), 725. https://doi.org/10.3390/s26020725

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop