Leveraging Multispectral Images for Cross-Camera Color Restoration

Color restoration, comprising white balance and color correction, is a core stage of the image signal processor (ISP) that maps the raw sensor response into a standard, device-independent color space. Yet with only three broadband RGB channels, disentangling illumination from reflectance is ill-posed, and these operations are tied to a camera-specific raw space that must be recalibrated for every new sensor. Multispectral (MS) sensors sample the spectrum more densely and provide a physical constraint that breaks this underdetermination. Existing methods still underuse this signal, as most apply it only to illuminant estimation, while the few that fuse multispectral image (MSI) with RGB across the whole pipeline train a separate model per camera and cannot transfer to unseen sensors. We present a physics-guided framework that uses a single fixed multispectral camera, treating the MSI as a *camera-agnostic spectral bridge* and exploiting it across the entire color-restoration pipeline. The three-stage framework predicts CSS and white-balance gains, followed by per-image color correction. Crucially, we optimize CSS in the sensor-response domain and use CSS projection to separate sensor dependence from illumination learning. We train the framework once, and it transfers to a new target RGB camera using only the DNG ColorMatrices of the target camera and a small set of MSI–RGB pairs, without retraining. Experimental results show that our framework outperforms existing cross-camera color-constancy and illuminant-estimation baselines and, in the cross-camera setting, approaches the color-restoration accuracy of single-camera MSI methods.