Zijun Wei

Optical remote sensing imagery is a vital data source for large-scale monitoring in polar regions. However, harsh imaging conditions often result in images with texture degradation. This imagery quality dilemma severely constrains the reliability of downstream analysis. Due to the uniqueness of the polar environment, current enhancement methods tend to introduce artifacts or suffer from limited domain generalization. To address these challenges, we propose a blind perceptual metric-driven enhancement (BPME) method for degraded polar optical imagery. Specifically, we first design a zero-reference metric system to precisely quantify degradation patterns. Subsequently, we construct the metric feature space to derive a blind perceptual metric prior (BPM-P) via clustering-based mapping. Finally, guided by the proposed BPM-P, a selective mechanism is employed to improve texture information adaptively. Comprehensive experiments confirm that the BPME outperforms state-of-the-art algorithms, as indicated by both qualitative and quantitative evaluations. The method also exhibits outstanding performance in downstream tasks such as contour extraction and ice flow velocity tracking. As a physically interpretable framework, BPME provides a robust solution for image degradation in polar remote sensing, effectively enhancing the interpretability and usability of optical remote sensing imagery.