Semantic-Guided Regularization to Mitigate Spectral Bias in Implicit Neural Representations

Implicit Neural Representation (INR) models signals as a function parameterized by a neural network, providing a continuous and concise alternative. In this study, we propose Semantic-Guided INR (SG-INR), a segmentation-based softguided approach designed to enhance image reconstruction by improving structural awareness during the learning process of 2D image-based INR. Existing INR models have introduced various methods to address spectral bias, but they still suffer from a tendency to favor low-frequency components. To address this limitation, we utilize a pre-trained semantic segmentation model to estimate structural regions in the image and generate a weighted guide mask. This mask selectively emphasizes semantically important regions during the optimization process. The proposed loss function mixes the original reconstruction loss with the structural awareness loss to give higher importance to key regions while allowing easy integration with existing INR frameworks. This proposal presents a novel approach to reconstructing high-resolution images and shows potential for advancement in quantitative metrics such as Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index Measure (SSIM).