Temporal Statistics and Heuristic Priors Based Perceptual Enhancement for FeO Prediction in Sintering

On-site monitoring of iron oxide (FeO) content is crucial for ensuring product quality in the steel sintering process. However, the harsh sintering environment, characterized by nonperiodicity, high dust levels, heavy smoke, and high-temperature steam, severely degrades the quality of images captured at the sintering machine tail, thereby impacting the accuracy of FeO content prediction. Existing methods for keyframe extraction and image enhancement often rely on benchmark clear images or fail to address the unique challenges of the sintering environment, such as nonperiodic operational uncertainties and low-light conditions. To overcome these limitations, this paper proposes a novel temporal statistics and heuristic priors-based perceptual enhancement (THPE) framework. The proposed framework integrates an innovative dual-analysis strategy for keyframe extraction and perceptual enhancement. Gaussian statistical modeling and time-series analysis are employed to accurately extract key information from videos, enabling robust handling of the nonperiodic and uncertain nature of the sintering process and ensuring reliable information capture. Furthermore, for some harsh sintering environments, the heuristic priors based perceptual enhancement approach is introduced, which incorporates the frequency-domain adjustment model (FDAM), atmospheric scattering model (ASM), and contrast-based model (CBM). This design enables effective low-light noise modeling and image enhancement without reliance on benchmark images. Experimental results demonstrate that the proposed framework can effectively improve the accuracy of keyframe extraction, enhance perceptual quality, and improve the prediction accuracy of FeO in sintering.