This research paper presents a novel class of restoration network architecture based on the Volterra series formulation. By incorporating non-linearity into the system response function through higher order convolutions instead of traditional activation functions, we introduce a general framework for image/video restoration. Through extensive experimentation, we demonstrate that our proposed architecture achieves state-of-the-art (SOTA) performance in the field of Image/Video Restoration. Moreover, we establish that the recently introduced Non-Linear Activation Free Network (NAF-NET) can be considered a special case within the broader class of Volterra Neural Networks. These findings highlight the potential of Volterra Neural Networks as a versatile and powerful tool for addressing complex restoration tasks in computer vision.

https://openaccess.thecvf.com/content/CVPR2024/papers/Roheda_MR-VNet_Media_Restoration_using_Volterra_Networks_CVPR_2024_paper.pdf