Fusion-Restoration Image Processing Algorithm to Improve the High-Temperature Deformation Measurement

2026-01-19

AI Summary

This study addresses the severe degradation of image quality in high-temperature environments caused by thermal radiation and heat haze, which significantly compromises the accuracy and effective computational domain of digital image correlation (DIC). To mitigate these effects, a synergistic fusion–restoration strategy is proposed: thermal radiation is suppressed through hierarchical image representation and parallel processing of positive–negative channels, while multi-exposure fusion enhances overall image quality. For high-frequency random errors induced by heat haze, restoration parameters are iteratively optimized using FSIM as the fidelity metric, and anomalous pixels are corrected via a grayscale mean algorithm. The proposed method markedly improves high-temperature DIC performance, expanding the effective computational area from 26%–32% to 40%–50% and reducing measurement errors in in-plane normal strains ε_xx and ε_yy and shear strain γ_xy by 85.3%, 36.0%, and 36.4%, respectively.

Technology Category

• Computer Vision: Image and Video Retrieval
• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Machine Learning: Calibration & Uncertainty Quantification

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Systems and Infrastructure for Web, Mobile and WoT: Web performance, measurement, and characterization
• Security and Privacy: Large-scale security measurements

Abstract

In the deformation measurement of high-temperature structures, image degradation caused by thermal radiation and random errors introduced by heat haze restrict the accuracy and effectiveness of deformation measurement. To suppress thermal radiation and heat haze using fusion-restoration image processing methods, thereby improving the accuracy and effectiveness of DIC in the measurement of high-temperature deformation. For image degradation caused by thermal radiation, based on the image layered representation, the image is decomposed into positive and negative channels for parallel processing, and then optimized for quality by multi-exposure image fusion. To counteract the high-frequency, random errors introduced by heat haze, we adopt the FSIM as the objective function to guide the iterative optimization of model parameters, and the grayscale average algorithm is applied to equalize anomalous gray values, thereby reducing measurement error. The proposed multi-exposure image fusion algorithm effectively suppresses image degradation caused by complex illumination conditions, boosting the effective computation area from 26% to 50% for under-exposed images and from 32% to 40% for over-exposed images without degrading measurement accuracy in the experiment. Meanwhile, the image restoration combined with the grayscale average algorithm reduces static thermal deformation measurement errors. The error in {\epsilon}_xx is reduced by 85.3%, while the errors in {\epsilon}_yy and {\gamma}_xy are reduced by 36.0% and 36.4%, respectively. We present image processing methods to suppress the interference of thermal radiation and heat haze in high-temperature deformation measurement using DIC. The experimental results verify that the proposed method can effectively improve image quality, reduce deformation measurement errors, and has potential application value in thermal deformation measurement.

Problem

• high-temperature deformation measurement
• thermal radiation
• heat haze
• image degradation
• measurement accuracy

Innovation

• multi-exposure image fusion
• image restoration
• heat haze suppression
• digital image correlation (DIC)
• thermal radiation compensation