Segmentation-Guided Sparse Transformer for Under-Display Camera Image Restoration

Under-display Camera is an emerging technology for full-screen display with a camera under the display. However, the current implementation of UDC causes serious image degradation. Incident light required for camera imaging undergoes attenuation and diffraction when passing through the display. Current UDC image restoration methods predominantly utilize convolutional networks, whereas transformer-based methods with superior performance are lacking. This paper proposes a Segmentation-Guided Sparse Transformer method (SGSFormer) for restoring images from UDC degraded images. Specifically, we utilize sparse self-attention to filter out redundant information and noise, directing the model’s attention to focus on the features more relevant to the degraded regions in need of reconstruction. Moreover, we integrate an instance segmentation map as prior information to guide sparse self-attention in filtering and focusing on the correct regions. Extensive experiments exhibit the superior performance of our model over the state-of-the-art methods.