Distribution-decouple learning network: an innovative approach for single image dehazing with spatial and frequency decoupling

Image dehazing methods face challenges in addressing the high coupling between haze and object feature distributions in the spatial and frequency domains. This coupling often results in oversharpening, color distortion, and blurring of details during the dehazing process. To address these issues, we introduce the distribution-decouple module (DDM) and dual-frequency attention mechanism (DFAM). The DDM works effectively in the spatial domain, decoupling haze and object features through a feature decoupler and then uses a two-stream modulator to further reduce the negative impact of haze on the distribution of object features. Simultaneously, the DFAM focuses on decoupling information in the frequency domain, separating high- and low-frequency information and applying attention to different frequency components for frequency calibration. Finally, we introduce a novel dehazing network, the distribution-decouple learning network for single image dehazing with spatial and frequency decoupling (DDLNet). This network integrates DDM and DFAM, effectively addressing the issue of coupled feature distributions in both spatial and frequency domains, thereby enhancing the clarity and fidelity of the dehazed images. Extensive experiments indicate the outperformance of our DDLNet when compared to the state-of-the-art (SOTA) methods, achieving a 1.50 dB increase in PSNR on the SOTS-indoor dataset. Concomitantly, it indicates a 1.26 dB boost on the SOTS-outdoor dataset. Additionally, our method performs significantly well on the nighttime dehazing dataset NHR, achieving a 0.91 dB improvement. Code and trained models are available at https://github.com/aoe-wyb/DDLNet.