Enhancing Aerial Image Texture for Better 3D Reconstruction: A Comparison of Histogram Equalization and Contrast Limited Adaptive Histogram Equalization Techniques
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Abstract
The quality of aerial imagery is often degraded due to factors such as adverse weather conditions, atmospheric interference, or camera limitation. These factors significantly impact the effectiveness of the three-dimensional reconstruction process, particularly during the feature extraction and point cloud formation stages. The problem becomes more pronounced when reconstructing relatively homogenous surfaces, where a lack of texture leads to poor feature matching, resulting in incomplete or inaccurate models in the Structure from Motion (SfM) workflow. This study aims to evaluate the effectiveness of two methods of improving image quality, Histogram Equalisation (HE) and Contrast Limited Adaptive Histogram Equalisation (CLAHE), in improving texture quality and consequently enhancing the accuracy and completeness of 3D reconstruction in homogeneous areas. The evaluation was conducted through visual analysis, point cloud density, and spatial comparison using a Cloud-to-Cloud (C2C) approach. The HE and CLAHE processed point clouds were compared against those derived from the original, unenhanced imagery. The results indicated that the CLAHE method provides a more controlled contrast increase and produces point clouds with improved density and distribution compared to HE and the original image. Furthermore, the C2C analysis revealed lower spatial deviations in the reconstructions generated using CLAHE. These findings suggest that CLAHE is more effective in enhancing visual quality, especially in low-textured areas, to support the reconstruction of photogrammetry-based mapping.
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References
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