Implicit Neural Representations (INRs) have revolutionized signal representation by leveraging neural networks to provide continuous and smooth representations of complex data. However, existing INRs face limitations in capturing fine-grained details, handling noise, and adapting to diverse signal types. To address these challenges, we introduce INCODE, a novel approach that enhances the control of the sinusoidal-based activation function in INRs using deep prior knowledge. INCODE comprises a harmonizer network and a composer network, where the harmonizer network dynamically adjusts key parameters of the activation function. Through a task-specific pre-trained model, INCODE adapts the task-specific parameters to optimize the representation process. Our approach not only excels in representation, but also extends its prowess to tackle complex tasks such as audio, image, and 3D shape reconstructions, as well as intricate challenges such as neural radiance fields (NeRFs), and inverse problems, including denoising, super-resolution, inpainting, and CT reconstruction. Through comprehensive experiments, INCODE demonstrates its superiority in terms of robustness, accuracy, quality, and convergence rate, broadening the scope of signal representation. Please visit the project's website for details on the proposed method and access to the code.

翻译：隐式神经表示通过利用神经网络对复杂数据提供连续平滑表示，已彻底革新了信号表示方式。然而，现有隐式神经表示在捕捉精细细节、处理噪声以及适应多样化信号类型方面仍存在局限性。针对这些挑战，我们提出INCODE方法，这是一种利用深度先验知识增强隐式神经表示中正弦基激活函数控制能力的新型方案。INCODE由协调器网络和组合器网络构成，其中协调器网络可动态调整激活函数的关键参数。通过任务特定的预训练模型，INCODE自适应优化任务相关参数以提升表示过程。该方法不仅在信号表示方面表现卓越，更将其优势延伸至音频、图像和三维形状重建等复杂任务，以及神经辐射场（NeRF）、逆问题（包括去噪、超分辨率、图像修复和CT重建）等具有挑战性的场景。通过全面实验验证，INCODE在鲁棒性、精确度、重建质量及收敛速度方面均展现出显著优势，拓宽了信号表示的应用范畴。项目网站提供了详细方法说明及代码资源。