Separate to Restore: Haze-Guided Feature Exchange for Image Dehazing

Single-image dehazing requires preserving scene content while suppressing haze-induced degradation. Modeling these objectives separately can encourage specialized representations, but restoration also requires communication between them. We propose a dual-branch framework that models clean scene content and a signed haze residual through separate pathways coordinated by haze-guided feature exchange. Haar wavelet cues provide complementary frequency biases, guiding the content branch toward structural details and the haze branch toward low-frequency degradation cues without imposing a fixed decomposition. Multiscale gated exchanges enable selective cross-branch communication. At the bottleneck, a routing module combines image-derived haze priors with learned corrections, prior-consistency weighting, and a learnable scaling factor to regulate where and how strongly messages are exchanged. Paired additive and subtractive updates redistribute features between the branches, while clean-image supervision, residual supervision, and input reconstruction consistency jointly encourage task-specific specialization. Under a controlled reimplementation protocol, the framework achieves 27.13 dB PSNR on O-Haze, exceeding the strongest compared baseline by 0.75 dB, with a mean PSNR of 26.98 ± 0.21 dB across five training seeds. Ablations and branch interventions suggest partial functional specialization, while evaluations on three additional real-haze benchmarks reveal remaining limitations under dense and nonuniform haze.