BRACE: Adversarial and Contrastive Endpoint Regularization for Schrödinger-Bridge Image Enhancement

Unpaired image enhancement can be viewed as transporting a degraded image distribution toward a clean one under a strong constraint of source-scene fidelity. Unlike broad domain translation, the output must undergo substantial appearance correction while remaining closely aligned with the input structure. Schrödinger-bridge methods provide a principled stochastic transport framework for this setting, but existing bridge-based objectives can be overly conservative, underconstrain target realism, and require multiple refinement steps at inference time. We propose Bridge Regularization with Adversarial and Contrastive Endpoints (BRACE), a Schrödinger-bridge method that strengthens endpoint supervision with explicit constraints for target realism and local structural consistency. The resulting translator preserves the bridge construction while producing more realistic and faithful outputs with very small inference budget. Across low-light enhancement, dehazing, and underwater enhancement benchmarks, BRACE consistently improves over a Schrödinger-bridge baseline and remains competitive with dedicated unpaired baselines. Our results indicate that explicit endpoint supervision enables effective bridge-based transport for image enhancement with few inference steps.