FreqOrtho-SR: Frequency-Guided Orthogonal Expert Learning for Real-World Image Super-Resolution

Diffusion prior-based methods have shown impressive resultsin real-world image super-resolution (ISR), yet two key challenges per-sist: balancing pixel-level fidelity with semantic quality, and adapting todiverse degradations. Existing dual-branch approaches freeze the pixelmodule during semantic training, but the semantic branch can still ex-pand capacity within the pixel subspace, precluding genuine perceptualimprovement. Moreover, using a single static adapter cannot generalizeacross heterogeneous real-world corruptions. To address both issues, wepropose FreqOrtho-SR, which comprises: Frequency-guided Mixture ofLoRA Experts (FreqMoE), it routes inputs to specialized experts via anon-parametric FFT-based degradation-feature extractor that encodesfrequency-domain signatures, enabling stable and interpretable special-ization across corruption types; and Orthogonal Gradient Projection(OGP), which reframes the dual-objective optimization as a subspace-constrained problem: by extracting the pixel-fidelity subspace via SVDon combined expert weight deltas and projecting semantic gradients ontoits null space, OGP guarantees orthogonality between the two objectives,enabling genuinely complementary learning without mutual interference.Experiments show that FreqOrtho-SR achieves competitive overall per-formance and a strong fidelity-perception trade-off across multiple bench-marks with efficient single-step inference. The source code of our methodcan be found at sonhm3029/FreqOrtho-SR.