Multi-Scale Texture Loss for CT denoising with GANs

arXiv:2403.16640v1 Announce Type: cross
Abstract: Generative Adversarial Networks (GANs) have proved as a powerful framework for denoising applications in medical imaging. However, GAN-based denoising algorithms still suffer from limitations in capturing complex relationships within the images. In this regard, the loss function plays a crucial role in guiding the image generation process, encompassing how much a synthetic image differs from a real image. To grasp highly complex and non-linear textural relationships in the training process, this work presents a loss …

arxiv cs.cv cs.lg denoising eess.iv gans loss scale texture type