Stereo-IDCE: Interactive Dual-Curve Estimation for Unsupervised Low-Light Stereo Image Enhancement

Enhancing low-light stereo images without supervised data faces significant challenges in effectively recovering brightness while preserving consistency between views. To tackle this, we propose Stereo-IDCE, the first unsupervised low-light stereo enhancement framework built upon interactive dual-curve estimation. Our approach simultaneously estimates pixel-wise curve parameter maps for both views and enforces photometric consistency through coupled feature interaction and specialized loss constraints. Specifically, we introduce a weight-shared IDCE-Net to extract illumination-aware features from both views, along with a cross-view feature interaction module that leverages structural and brightness cues from the opposite view to enable collaborative curve estimation. This design ensures that enhancement operations are coordinated across views rather than learned independently. For loss constraints, we design a joint curve estimation loss CDE-Loss to minimize discrepancies between the left and right curve parameter maps, and a cross-view exposure control loss to align the average brightness of corresponding local regions. These losses explicitly preserve photometric consistency without requiring ground-truth. Extensive experiments on synthetic and in-the-wild datasets demonstrate that our method outperforms existing baselines in enhancing low-light stereo images.