R2AS: Relative-to-Absolute Supervision for Real-World Dehazing

Adapting image dehazing models to unlabeled real images requires supervision beyond physical reconstruction: different combinations of transmission and clean radiance can explain the same hazy image. Under a shared-atmospheric-light model, controlled haze interventions prescribe exact relative optical-depth shifts between views. This separates the inverse problem into a known relative structure and one unknown absolute offset per pixel. We propose Relative-to-Absolute Supervision (R2AS) for real-world dehazing. First, Relative-State Calibration (RSC) constructs views with known transmission scaling and trains a teacher to match their prescribed optical-depth differences while enforcing reconstruction compatibility with the observations. Second, Absolute-State Anchoring (ASA) projects the teacher's aligned transmission and radiance predictions onto a shared, observation-compatible family, producing joint targets through a closed-form scalar solution at each pixel. The calibrated teacher remains frozen while these targets supervise a student that retains the backbone architecture and requires a single dehazer evaluation at inference. We establish a bound on relative optical-depth error and prove that anchoring does not increase weighted joint target error when the true state lies in the feasible family. Experiments on six real-world benchmarks demonstrate that R2AS achieves the highest MUSIQ on all four unpaired datasets and the highest PSNR on both paired datasets among the compared methods.