7‐5: Synthetic Dataset for Improving UDC Video Restoration Network Performance

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Abstract

Under‐display camera (UDC) is a new form factor that implements a full‐screen display by locating the camera under the display panel. UDC suffer from image degradation and flare due to the panel in front of the camera. In particular, flare cannot be restored by conventional image processing due to the saturated pixels. Therefore, deep learning network that can restore these degradations is proposed. The network is trained by synthetic dataset generated by accurate optical simulation. However, the deep learning networks might not be suitable for video calls that require real‐time image processing due to their large amount of computation. The guided filter methodology is adapted to the network to reduce the amount of computation. Total amount of calculation is reduce by 90% and achieved 30 FPS at the FHD (1920×1080×3) resolution. In this paper, the method to generate realistic synthetic dataset that can solve the problems occurred during video restoration.

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