City Scene Super-Resolution via Geometric Error Minimization

Zhengyang Lu; Feng Wang

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arXiv · 2401.07272

City Scene Super-Resolution via Geometric Error Minimization

Zhengyang Lu·Feng Wang

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Abstract

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.

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Zhengyang Lu, Feng Wang. 2024-01-14. City Scene Super-Resolution via Geometric Error Minimization. https://arxiv.org/abs/2401.07272

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