DDPR: Dominant Degradation Planning with Latent Residual-Guided Adaptive Editing for All-in-One Image Restoration

Generative models have shown strong potential for All-in-One image restoration. However, existing methods either process compound degradations in a single pass or lack dynamic reassessment of the dominant degradation, often leading to suboptimal restoration orders and residual degradations. They also apply uniform restoration across the image, without distinguishing regions requiring content preservation from those requiring stronger restoration, which may cause content hallucination. To address these limitations, we propose Dominant Degradation Planning for image Restoration (DDPR), a unified framework with latent residual-guided adaptive editing. Specifically, the Dominant Degradation Planner identifies the current dominant degradation and reassesses the image after each restoration step, decomposing compound degradations into a dynamically planned sequence of restoration tasks. The Degradation-Aware Latent Residual Assessor predicts a region-level degradation gate from VAE latent residuals, guiding the Degradation-Gated Dual-Branch LoRA to adaptively balance the content-preservation and degradation-restoration branches. Experiments demonstrate that DDPR achieves SOTA performance among open-source methods across both synthetic and real-world degradation benchmarks. Code will be publicly released.