  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
