Implicit Neural Representation for Video Restoration
This addresses the flexibility issue in video restoration for applications requiring high-resolution videos, though it is incremental as it builds on existing INR techniques.
The paper tackles the problem of video restoration by introducing VR-INR, a method based on Implicit Neural Representations that generalizes to arbitrary super-resolution scales and performs zero-shot denoising, achieving high-quality reconstructions and outperforming state-of-the-art approaches in sharpness, detail preservation, and denoising efficacy.
High-resolution (HR) videos play a crucial role in many computer vision applications. Although existing video restoration (VR) methods can significantly enhance video quality by exploiting temporal information across video frames, they are typically trained for fixed upscaling factors and lack the flexibility to handle scales or degradations beyond their training distribution. In this paper, we introduce VR-INR, a novel video restoration approach based on Implicit Neural Representations (INRs) that is trained only on a single upscaling factor ($\times 4$) but generalizes effectively to arbitrary, unseen super-resolution scales at test time. Notably, VR-INR also performs zero-shot denoising on noisy input, despite never having seen noisy data during training. Our method employs a hierarchical spatial-temporal-texture encoding framework coupled with multi-resolution implicit hash encoding, enabling adaptive decoding of high-resolution and noise-suppressed frames from low-resolution inputs at any desired magnification. Experimental results show that VR-INR consistently maintains high-quality reconstructions at unseen scales and noise during training, significantly outperforming state-of-the-art approaches in sharpness, detail preservation, and denoising efficacy.