CL-ODGAN: an unpaired attention-guided GAN framework for remote sensing image dehazing

Haze in remote sensing imagery severely degrades image quality and impedes accurate interpretation. While deep learning has advanced dehazing, its reliance on large-scale paired data is impractical for remote sensing applications. To overcome this, we propose CL-ODGAN, a novel unpaired dehazing framework built upon a systematically designed generative architecture and a dual-level contrastive learning constraint. Our framework is architected around three synergistic components: (1) A robust generator that employs custom Residual blocks embedded with Omni-dimensional Dynamic Convolution (ODConv) for dynamic multi-scale perception and a hybrid normalization (BN + IN) strategy for training stability. This design enables adaptive feature extraction and detail restoration under non-uniform haze. (2) A core unsupervised supervisory signal implemented via a dual-level (feature and pixel) contrastive learning mechanism within the GAN framework. This mechanism guides the image translation process without paired data by enforcing structural and feature consistency. (3) An attention-guided adversarial training paradigm that focuses the network on critical hazy regions, ensuring efficient learning. In essence, CL-ODGAN integrates ODConv and hybrid normalization withinthe generator to enhance its representational capacity, while employing contrastive learning as the supervisory engine-all orchestrated under an unpaired, attention-aware GAN framework. Experiments demonstrate its superior performance in removing non-uniform haze and recovering fine details without requiring paired training data.