Medical image super-resolution reconstruction based on large kernel attention cycle network
Medical imaging plays an important role in the clinical diagnosis of doctors.Due to the limitation of imaging principle and equipment,many times the images obtained are not ideal.Introducing super-resolution technology into the field of medical images and using large kernel decomposition and attention mechanism in super-resolution tasks can make convolutional neural network not inferior to the method based on Transformer.In this paper,a super-resolution method for medical images based on cycle generative adversarial networks is proposed.The large kernel attention mechanism is used to improve the image quality,and the cycle generative adversarial network is used to improve the quality and accuracy of image detail recovery.
large kernel decompositioncycle generative adversarial networkmedical imagessuper-resolution reconstructionattention mechanism
NETL
NSTL
万方数据
维普
