DynaOrtho-SR: Online Orthogonal Co-Evolution for Real-World Image Super-Resolution

Real-world image super-resolution based on pre-trained diffusion models requires balancing faithful restoration with natural detail synthesis. Spatially non-uniform degradations demand region-adaptive restoration, while continued optimization of the pixel restoration branch can cause a fixed subspace constraint to become misaligned with its evolving representation. We propose DynaOrtho-SR, a one-step diffusion framework that integrates local degradation-aware expert modeling with online orthogonal co-evolution (OOCE). A local degradation-aware convolutional mixture-of-experts (C-MoE) jointly exploits intermediate features and local degradation descriptors for spatially adaptive routing. During alternating optimization of pixel experts and semantic adapters, we periodically update the pixel restoration subspace and constrain semantic updates through gradient projection and adapter-weight retraction. Since parameter-space orthogonality does not necessarily ensure functional complementarity, we further introduce functional orthogonality regularization (F-Ortho) on shared probes to suppress correlations between branch responses, providing complementary constraints in parameter and function spaces. At inference time, adjusting branch contributions yields different trade-offs between fidelity and perceptual quality without retraining or additional iterative diffusion sampling steps. Experiments on synthetic and real-world degradation benchmarks demonstrate competitive reconstruction performance, with a favorable balance between reconstruction fidelity and reference-based perceptual quality on real-world benchmarks.