Attention-based U-Net for image demoiréing

Main Article Content

Tomasz Lehmann


Keywords : image demoireing, computer vision, attention U-Net, cross-sampling
Abstract

Image demoiréing is a particular example of a picture restoration problem. Moiré is an interference pattern generated by overlaying similar but slightly offset templates.
In this paper, we present a deep learning based algorithm to reduce moiré disruptions. The proposed solution contains an explanation of the cross-sampling procedure – the training dataset management method which was optimized according to limited computing resources.
Suggested neural network architecture is based on Attention U-Net structure. It is an exceptionally effective model which was not proposed before in image demoiréing systems. The greatest improvement of this model in comparison to U-Net network is the implementation of attention gates. These additional computing operations make the algorithm more focused on target structures.
We also examined three MSE and SSIM based loss functions. The SSIM index is used to predict the perceived quality of digital images and videos. A similar approach was applied in various computer vision areas.
The author’s main contributions to the image demoiréing problem contain the use of the novel architecture for this task, innovative two-part loss function, and the untypical use of the cross-sampling training procedure.

Article Details

How to Cite
Lehmann, T. (2022). Attention-based U-Net for image demoiréing. Machine Graphics and Vision, 31(1/4), 3–17. https://doi.org/10.22630/MGV.2022.31.1.1
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