City Scene Super-Resolution via Geometric Error Minimization
Zhengyang Lu Feng Wang
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.