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Biological and automated vision systems which use digital video for navigation depend on the video to be of sufficient quality in order to extract reliable information that can inform the guidance and/or other decision-making processes. Although systems are available for detection and mitigation of digital distortions (e.g., compression, packet loss), detection and mitigation of natural distortions such as glare, rain, and fog have received much less attention. In this paper, we address the issue of glare detection in a single captured frame. We propose an algorithm which uses a combination of simple and efficient photometric, colorimetric, and GPS features to detect the location and spatial extent of glare within captured images. Specifically, feature maps using lightness, saturation, contrast, and color distance are computed, combined, and then, refined based on the sun's predicted location from the GPS information. In addition, we present a new ground-truth database for glare detection, in which the location, extent, and severity of glare was rated by human subjects for a collection of images. Testing on our ground-truth database revealed that the proposed algorithm can reliably detect the locations and spatial extents of glare sources in a variety of images based on subjective ratings and well-known quantitative measures.

An algorithm for glare detection via photometric, colorimetric, and global positioning features