  Despite advances in the paradigm of pre-training then fine-tuning in
low-level vision tasks, significant challenges persist particularly regarding
the increased size of pre-trained models such as memory usage and training
time. Another concern often encountered is the unsatisfying results yielded
when directly applying pre-trained single-image models to multi-image domain.
In this paper, we propose a efficient method for transferring a pre-trained
single-image super-resolution (SISR) transformer network to the domain of
stereo image super-resolution (SteISR) through a parameter-efficient
fine-tuning (PEFT) method. Specifically, we introduce the concept of stereo
adapters and spatial adapters which are incorporated into the pre-trained SISR
transformer network. Subsequently, the pre-trained SISR model is frozen,
enabling us to fine-tune the adapters using stereo datasets along. By adopting
this training method, we enhance the ability of the SISR model to accurately
infer stereo images by 0.79dB on the Flickr1024 dataset. This method allows us
to train only 4.8% of the original model parameters, achieving state-of-the-art
performance on four commonly used SteISR benchmarks. Compared to the more
complicated full fine-tuning approach, our method reduces training time and
memory consumption by 57% and 15%, respectively.
