Adapting low-dose CT denoisers for texture preservation using zero-shot local noise-level matching.

Background: On enhancing the image quality of low-dose computed tomography (LDCT), various denoising methods have achieved meaningful improvements. However, they commonly produce over-smoothed results; the denoised images tend to be more blurred than the normal-dose targets (NDCTs). Furthermore, many recent denoising methods employ deep learning(DL)-based models, which require a vast amount of CT images (or image pairs).