Learning the degradation distribution for medical image superresolution via sparse swin transformer

Xianjun Han, Zhaoyang Xie, Qianqian Chen, Xuejun Li, Hongyu Yang

Abstract: Highlights•We introduce a probabilistic degradation to model the random factors in real scenarios. This model combines natural images and medical images for training, which alleviates insufficient medical images and enables the generated low-resolution medical images to learn the authentic degradation process in natural images.•We proposed a new medical SR model, SSFormer, which integrates transformer components and CNN modules into a united framework to capture the long-range dependencies and local perception.•We use the nonlocal attention mechanism to achieve a similar effect. Meanwhile, to reduce the computational cost, the locality sensitive hashing is employed in the nonlocal attention module by projecting extracted features onto a spherical hyperplane for similarity calculations.