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.

INCODE 是一种利用深度先验知识增强 INR 中正弦基准激活函数的控制的新方法，它通过调整激活函数的关键参数来优化表示过程，不仅在表示方面出色，而且在处理音频、图像、3D 形状重建以及神经辐射场（NeRFs）和逆问题（包括降噪、超分辨率、修复和 CT 重建）等复杂任务方面具有突出表现，从而拓宽了信号表示的范围。