Negative Restoration via the Heterogeneity of Channel-Induced Geometric Features

Color film negatives are vulnerable to channel-heterogeneous degredation caused by long-term chemical aging, where the blue (B) channel is often substantially more corrupted than the relatively intact red (R) and green (G) channels. Existing restoration methods typically process degraded RGB images holistically and therefore fail to explicitly exploit such cross-channel structural asymmetry. In this work, we propose a geometry-guided approach for negative-film restoration based on a key observation: different color channels depict the same underlying scene geometry and should yield consistent geometric features extracted from the pre-trained 3D foundation model in reliable regions, whereas deterioration introduces cross-channel feature discrepancies. We exploit these discrepancies to localize unreliable B-channel regions and guide selective restoration using information from the other two intact channels while preserving already reliable content. We further utilize the geometric features to facilitate reference-conditioned color mapping by identifying geometrically and semantically related regions between the restored negative and a reference printed image, enabling region-aware color mapping guided by the reference. Extensive experiments demonstrate that our approach achieves superior negative-film restoration and more coherent color mapping than combinations of existing restoration and color-mapping methods.