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

翻译：隐式神经表示（INR）近年来在计算机视觉领域逐渐兴起。已有研究表明，它在参数化连续信号（例如从离散图像数据中生成密集三维模型，如神经辐射场NeRF）方面具有有效性。然而，INR在二维图像处理任务中的探索尚不充分。考虑到INR的基本定义和结构，我们对其在图像恢复等低级视觉问题中的有效性感兴趣。本文重新审视了INR，并研究了其在低级图像恢复任务（包括图像去噪、超分辨率、修复和去模糊）中的应用。广泛的实验评估表明，INR在资源有限的多个低级视觉任务中表现出优越性能，相比同类方法提升超过2dB。代码和模型已开源至 https://github.com/WenTXuL/LINR