Deblurring and denoising a medical image such as X-ray fluoroscopy images may be challenging, and deep-learning based techniques may be employed to meet the challenge. An artificial neural network (ANN) may be trained using training images with synthetic noise and as well as training images with real noise. The parameters of the ANN may be adjusted during the training based on at least a first loss designed to maintain continuity between consecutive medical images generated by the ANN and a second loss designed to maintain similarity of patches inside a medical image generated by the ANN. The parameters of the ANN may be further adjusted based on a third loss that may be calculated from ground truth associated with the synthetic training images. Transfer learning between the synthetic images and the real images may be accomplished using these techniques.