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P-polarizedlight

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you can see that there is the main specular spot in the middle of the image and some minor ones around. I am mostly interested for the main cases but if I could extract the other smaller cases as well that would be perfect.

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@pklab thanks for the answer, however my main problem is not recovering the specular spot but first detecting it. For recovering, inpaint() could be one option but I have come upon some nice techniques (I do not remember now the sources) that it was used for recovering pixel intensity from shadow areas. Also the paper that you suggest could be an option. However, detecting the specular hotspots seems not be that easy as it seems. Moreover, unfortunately hardware-wise there is not the possibility something to be done. You get my upvote, though ;-).

Fast and High Quality Highlight Removal from A Single Image which says that outperforms the previous both but I unfortunately they do not share the code at the moment. I am in doubts, because the datasets that they use of evaluation are not that close to a real life case like the one I am showing above for that reason I was asking in case someone has come up with something else.

You can't get back information you lost due to pixel saturation. All you can do is to try some synthetic reconstruction using information from neighbour like cv::inpaint function does. Just to show, below is a simple code that use uses cv::inpaint.

I know it's a completely total different approach, but if you can control camera/acquisition, you could try to reduce the brightness/gain/shutter down to remove spotlight...

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I don't have a ready to use solution... sure it isn't easy but your could start from @Guanta answer here, this paper Real-Time Specular Highlight Removal Using Bilateral Filtering and this also is nice A Survey of Specularity Removal Methods.

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Spotlights have low saturation and high brightness such as overexposure or some pure white objects. May be shape and gradient could be useful to do basic segmentation... it would be interesting to build a machine learning approach around this problem. have fun :)

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2020430 — Dark field microscopy is a type of microscopy technique that is used in both light and electron microscopy, where only the specimen is lit by a light or ...

you see that it detects the main specular spot but at the same time anything else white-ish is a candidate for detected as specularity as well.

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@pklab no problem, no need for worries :-). I am aware of @Guanta 's answer as well as the papers that you suggest, though they seem a bit outdated. As I mentioned in my initial post I have already tried the following papers Real-time highlight removal using intensity ratio and here is the code and Efficient and Robust Highlight Removal and code here which seem to be included in the state of the art at the moment but without that good results. Then there is also this work:

Searching around the literature I found the following 3 interesting papers: Real-time highlight removal using intensity ratio, Efficient and Robust Specular Highlight Removal and Fast and High Quality Highlight Removal from A Single Image. The first two that they provide the code as well gave not that good results.

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It would be easier to recover dark region if a bit of information will survive see Automated Removal of Partial Occlusion Blur for reference.

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Using more than one image is another approach, this Improved seeded region growing for detection of water bodies in aerial images is a bit different but could be a starting point.

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When doing visual work with Lunt White Light Wedges, we recommend adding a Polarizing Filter for more comfortable viewing.  We offer both 1.25″ & 2″ Polarizing Filters.  Choose the one that matches the barrel size of your White Light Wedge.  Simply screw the filter onto the barrel of your eyepiece, and then adjust the brightness of the scene by rotating the top-most filter.

Hi guys, I am interested to find a way to detect and remove the specularities from a given indoors image. For example give the following image:

where it seems to be a specularity but in reality it's an overexposure. Therefore, I would like to hear your ideas regarding how I could optimize the detection or if you have any other implementation/paper that might help. The idea is to create a specular hightlights map and recover afterwards the intensity of pixels, pointed from the map. Thanks.