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Implicit Neural Representation (INR) has been emerging in computer vision inrecent years. It has been shown to be effective in parameterising continuoussignals such as dense 3D models from discrete image data, e.g. the neuralradius field (NeRF). However, INR is under-explored in 2D image processingtasks. Considering the basic definition and the structure of INR, we areinterested in its effectiveness in low-level vision problems such as imagerestoration. In this work, we revisit INR and investigate its application inlow-level image restoration tasks including image denoising, super-resolution,inpainting, and deblurring. Extensive experimental evaluations suggest thesuperior performance of INR in several low-level vision tasks with limitedresources, outperforming its counterparts by over 2dB. Code and models areavailable at https://github.com/WenTXuL/LINR

Revisiting Implicit Neural Representations in Low-Level Vision