getting details in images. In particular, we are applying this type of model for enhancement, to get a better quality of images. Since I'm really into nature, I wanted to understand if it were possible to use with underwater images. Underwater images are really fantastic but usually they are really noisy. The colors are not there. Everything is blue, green. It depends on the depth and that is really terrible when you are doing a mission to understand what is underwater, which type of species, what is in front of you. The method consisted in taking images from the subsurface, extract the features, extract the main components of the images, and try to understand the role of these features with capsule layers , in order to know if the features together belong to an object or they are playing a role in the scene. And then reconstruct the images just from the feature: reconstructing the images is learning to take into consideration the features that are used to reconstruct the information of the object and the colors removing the noise. The idea is to have the first part, the extraction, and the second part, the reconstruction, completely isolated. They collaborate, but the extraction doesn't need the second part. That means that it can be also used as a compressor of images, so embedded in the robot underwater, and then when the campaign is finished, we can collect just the features of the image and reconstruct all the images. I know scientists whot have been working many years on underwater images: Derya Akkaynak in Eilat and Tali Treibitz in Haifa. Yeah, I have seen lots of talks from them. Yeah, yeah. They are working more on the physics, also. That is behind the fact that the color is changing and they use a lot of the location because if you have all this information about the location, the depth, the moment in the day, you can really use physics to remove water and you have beautiful color. On the other hand, when you don't have all this information but you have just the image, you need just to guess what is inside. So we are training the model to, let's say, guess - but we hope that it's learning how to construct. 23 DAILY WACV Monday Rita Pucci
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