Removing specular reflection from multiple images by 'merging' images

Question

The system I'm working on uses a mobile phone app to take images. The white speckles in the images are reflective particles that need to be activated by the flash on the mobile phone for cataloging in an image processing pipeline. The downside is that we get unwanted specular reflection from the plastic in which the reflective particles are embedded. So the idea is that by taking multiple images and somehow 'stitching' them together the speckles could be preserved and the unwanted specular reflection removed to create one final 'clean' image.

I haven't been able to find any existing imaging processing techniques in the literature that use this approach but it seems like it might work. Any pointers on this approach would be much appreciated be it papers, pseudo-code or open source projects.

Answer 1

I'm not aware of specific work on the subject, but seems that this can be solved using standard approaches.

By the images it looks that the specularity can be easily detected simply based on graylevels (a large light blob) at least in some cases.

To be fused the images need to be registered. Could initialize / sanity-check using the cellphone odometry if available, then refine it - estimating a homography using RANSAC (assuming you're dealing with approx. planar images as in your example).

Getting data association with sufficient inliers can be a challenge, but perhaps odometry will help here. Also will probably need to fiddle with the image to get good features.