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 unsupervisedly pre-trained on the denoising task, SiamTrans is tested on three different low-level vision tasks (deraining, demoireing, and desnowing).

Compared with related methods, SiamTrans achieves the best performances, even outperforming those with supervised learning.