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arXiv Computer Vision By Sumit Kumar Dam, Mrityunjoy Gain, Eui-Nam Huh, Choong Seon Hong

High-Frequency First: A Two-Stage Approach for Improving Image INR

Read the original on arXiv Computer Vision →

The paper proposes a two-stage training strategy for Implicit Neural Representations (INRs) that addresses spectral bias by using a neighbor-aware soft mask to emphasize high-frequency details early in training. In the first stage, the mask assigns higher weights to pixels with strong local variations, encouraging the network to focus on fine edges and textures. The second stage transitions to full-image training, and experiments show consistent improvements in reconstruction quality across existing INR methods.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computer Vision.

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