DORIAN: Zero-Shot Directionally Structured Noise Removal via Graph Neural Priors in Fourier Space

Various scientific imaging modalities, for instance, light-sheet fluorescence microscopy, atomic force microscopy, and remote sensing, suffer from degradations that persist along a dominant orientation over image-scale distances. We refer to them as Directionally Persistent Structured Noise (DPSN). Three characteristics render DPSN difficult to remove: it is non-local in the image domain, its spectrum overlaps with that of the underlying image, and its appearance varies across modalities in which clean references are rarely available. Motivated by its compact representation in the Fourier domain, we propose DORIAN, a zero-shot framework that poses DPSN removal as a Fourier-domain spectral reconstruction problem. Specifically, we construct a polar graph over Fourier coefficients, on which a graph neural prior reconstructs corrupted coefficients from spectrally related uncorrupted ones. The graph prior is optimized on each degraded observation alone, requiring neither external training data nor clean targets. Experiments on natural images, microscopy, and remote sensing data show that DORIAN removes DPSN of diverse physical origins and appearances within a single framework.