Kavli Affiliate: Cheng Peng
| First 5 Authors: Yuan Ni, Yuan Ni, , ,
| Summary:
Implicit neural representations (INRs) have emerged as a compact and
parametric alternative to discrete array-based data representations, encoding
information directly in neural network weights to enable resolution-independent
representation and memory efficiency. However, existing INR approaches, when
constrained to compact network sizes, struggle to faithfully represent the
multi-scale structures, high-frequency information, and fine textures that
characterize the majority of scientific datasets. To address this limitation,
we propose WIEN-INR, a wavelet-informed implicit neural representation that
distributes modeling across different resolution scales and employs a
specialized kernel network at the finest scale to recover subtle details. This
multi-scale architecture allows for the use of smaller networks to retain the
full spectrum of information while preserving the training efficiency and
reducing storage cost. Through extensive experiments on diverse scientific
datasets spanning different scales and structural complexities, WIEN-INR
achieves superior reconstruction fidelity while maintaining a compact model
size. These results demonstrate WIEN-INR as a practical neural representation
framework for high-fidelity scientific data encoding, extending the
applicability of INRs to domains where efficient preservation of fine detail is
essential.
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