The Effect of CLAHE preprocessing on stitching beach panorama images with lighting variations

Coastal surveillance is a crucial aspect in maintaining maritime security and ecological sustainability, as coastal areas are vulnerable to various threats, both from the environment and human activities. This study focuses on improving the quality of surveillance through image stitching techniques for images taken in coastal environments with varying lighting conditions. We apply the Contrast Limited Adaptive Histogram Equalization (CLAHE) algorithm as a preprocessing step to improve image contrast before stitching using Scale-Invariant Feature Transform (SIFT). This process aims to improve the quality of the resulting panorama, especially in environments with inconsistent lighting, such as beaches. Experimental results show that preprocessing with CLAHE can increase the number of matched points in the stitching process by 47.53%, indicating a significant increase in image contrast and detail. By increasing the number of identifiable and matched features, this method allows for more accurate image stitching and produces higher-quality panoramas. These findings emphasize the importance of preprocessing in improving the effectiveness of image stitching, especially in dealing with the challenges presented by lighting variations in coastal environments.