Sharing an INR's weights across frames and modulating only per-frame biases via a time-conditioned hypernetwork gives a compact continuous video representation that outperforms prior video INRs on interpolation, super-resolution, denoising, and inpainting.
Revisiting Implicit Neural Representations in Low-Level Vision
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abstract
Implicit Neural Representation (INR) has been emerging in computer vision in recent years. It has been shown to be effective in parameterising continuous signals such as dense 3D models from discrete image data, e.g. the neural radius field (NeRF). However, INR is under-explored in 2D image processing tasks. Considering the basic definition and the structure of INR, we are interested in its effectiveness in low-level vision problems such as image restoration. In this work, we revisit INR and investigate its application in low-level image restoration tasks including image denoising, super-resolution, inpainting, and deblurring. Extensive experimental evaluations suggest the superior performance of INR in several low-level vision tasks with limited resources, outperforming its counterparts by over 2dB. Code and models are available at https://github.com/WenTXuL/LINR
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Bias for Action: Video Implicit Neural Representations with Bias Modulation
Sharing an INR's weights across frames and modulating only per-frame biases via a time-conditioned hypernetwork gives a compact continuous video representation that outperforms prior video INRs on interpolation, super-resolution, denoising, and inpainting.