Retaining Image Feature Matching Performance Under Low Light Conditions
This work addresses the challenge of maintaining image feature matching for applications like computer vision in low light conditions, but it is incremental as it builds on existing methods without introducing new paradigms.
The paper tackled the problem of reduced feature matching in low light images by investigating the impact of adjusting feature acceptance thresholds and applying Low Light Image Enhancement (LLIE) pre-processing, finding that lowering thresholds maintains reasonable performance and LLIE further improves matching when paired with suitable feature extraction algorithms.
Poor image quality in low light images may result in a reduced number of feature matching between images. In this paper, we investigate the performance of feature extraction algorithms in low light environments. To find an optimal setting to retain feature matching performance in low light images, we look into the effect of changing feature acceptance threshold for feature detector and adding pre-processing in the form of Low Light Image Enhancement (LLIE) prior to feature detection. We observe that even in low light images, feature matching using traditional hand-crafted feature detectors still performs reasonably well by lowering the threshold parameter. We also show that applying Low Light Image Enhancement (LLIE) algorithms can improve feature matching even more when paired with the right feature extraction algorithm.