City Scene Super-Resolution via Geometric Error Minimization
This improves image granularity for data-informed cultural heritage applications, though it appears incremental as it builds on existing super-resolution methods with a geometric focus.
The paper tackles super-resolution for urban scenes by minimizing geometric errors to preserve structural regularities, achieving state-of-the-art performance on multiple datasets including Cityscapes and GSV-Cities.
Super-resolution techniques are crucial in improving image granularity, particularly in complex urban scenes, where preserving geometric structures is vital for data-informed cultural heritage applications. In this paper, we propose a city scene super-resolution method via geometric error minimization. The geometric-consistent mechanism leverages the Hough Transform to extract regular geometric features in city scenes, enabling the computation of geometric errors between low-resolution and high-resolution images. By minimizing mixed mean square error and geometric align error during the super-resolution process, the proposed method efficiently restores details and geometric regularities. Extensive validations on the SET14, BSD300, Cityscapes and GSV-Cities datasets demonstrate that the proposed method outperforms existing state-of-the-art methods, especially in urban scenes.