SiamTrans: Zero-Shot Multi-Frame Image Restoration with Pre-Trained Siamese Transformers

• URL: http://arxiv.org/abs/2112.09426v1
• Date: Fri, 17 Dec 2021 10:42:39 GMT
• Title: SiamTrans: Zero-Shot Multi-Frame Image Restoration with Pre-Trained Siamese Transformers
• Authors: Lin Liu, Shanxin Yuan, Jianzhuang Liu, Xin Guo, Youliang Yan, Qi Tian
• Abstract summary: We propose a novel zero-shot multi-frame image restoration method for removing unwanted obstruction elements. It has three stages: transformer pre-training, zero-shot restoration, and hard patch refinement. For zero-shot image restoration, we design a novel model, termed SiamTrans, which is constructed by Siamese transformers, encoders, and decoders.
• Score: 95.57829796484472
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: We propose a novel zero-shot multi-frame image restoration method for removing unwanted obstruction elements (such as rains, snow, and moire patterns) that vary in successive frames. It has three stages: transformer pre-training, zero-shot restoration, and hard patch refinement. Using the pre-trained transformers, our model is able to tell the motion difference between the true image information and the obstructing elements. For zero-shot image restoration, we design a novel model, termed SiamTrans, which is constructed by Siamese transformers, encoders, and decoders. Each transformer has a temporal attention layer and several self-attention layers, to capture both temporal and spatial information of multiple frames. Only pre-trained (self-supervised) on the denoising task, SiamTrans is tested on three different low-level vision tasks (deraining, demoireing, and desnowing). Compared with related methods, ours achieves the best performances, even outperforming those with supervised learning.

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