The invention belongs to the field of medical
image processing, and provides a differential CT
image enhancement method based on
noise level, which comprises six steps of data preprocessing, construction and training of a denoising network, construction and training of a super-resolution network, construction and training of a
noise level classification network, CT
image enhancement and data post-
processing. According to the method, the
noise level of the CT image is judged, different
deep learning algorithms, namely a denoising or super-resolution model, are used for optimizing the quality of the CT image, the generalization ability is higher, and more complex and variable input data can be dealt with; data with noise are processed by using various traditional filtering methods and super-resolution pre-training models to obtain
gold standard images required by training, so that time and energy are not required to be spent on collecting high-quality
gold standard images in advance; the CT value of the output image is calibrated through post-
processing operation, it is ensured that the CT value difference before and after enhancement does not affect the accuracy of
clinical diagnosis, and compared with other methods without CT value calibration, the method has more practical application value.