Focal stacking is a technique that allows us to create images with a large
depth of field, where everything in the scene is sharp and clear. However, creating
such images is not easy, as it requires taking multiple pictures at different focus
settings and then blending them together. In this paper, we present a novel
approach to blending a focal stack using a special type of autoencoder, which is a
neural network that can learn to compress and reconstruct data. Our autoencoder
consists of several parts, each of which processes one input image and passes its
information to the final part, which fuses them into one output image. Unlike
other methods, our approach is capable of inpainting and denoising resulting in
sharp, clean all-in-focus images. Our approach does not require any prior training
or a large dataset, which makes it fast and effective. We evaluate our method
on various kinds of images and compare it with other widely used methods. We
demonstrate that our method can produce superior focal stacked images with
higher accuracy and quality. This paper reveals a new and promising way of using
a neural network to aid in microphotography, microscopy, and visual computing,
by enhancing the quality of focal stacked images.
| Identifer | oai:union.ndltd.org:kaust.edu.sa/oai:repository.kaust.edu.sa:10754/693633 |
| Date | 07 1900 |
| Creators | Al Nasser, Ali |
| Contributors | Wonka, Peter, Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division, Feron, Eric, Moshkov, Mikhail |
| Source Sets | King Abdullah University of Science and Technology |
| Language | English |
| Detected Language | English |
| Type | Thesis |
| Rights | 2024-08-20, At the time of archiving, the student author of this thesis opted to temporarily restrict access to it. The full text of this thesis will become available to the public after the expiration of the embargo on 2024-08-20. |
| Relation | https://github.com/Alooi/focal_AI_stacking |
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