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MR-VNet: Media Restoration using Volterra Networks

Siddharth Roheda · Amit Unde · Loay Rashid

Arch 4A-E Poster #125
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Wed 19 Jun 5 p.m. PDT — 6:30 p.m. PDT


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.

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