DKS-Net:a depthwise kernel selective network for single image dehazing

Abstract

The performance of deep learning-based dehazing frameworks inevitably degrades when applied to real-world scenarios, primarily due to the severe domain shift and diverse degradation types. A major bottleneck is that existing literature heavily relies on the raw pixel domain, thereby neglecting the distinct spectral characteristics of hazy observations and underutilizing the latent representation capacity of deep networks for high-quality image reconstruction. Concurrently, while attention-driven feature fusion has advanced image restoration, current formulations often suffer from inadequate dimension-wise feature interactions and coarse integration strategies. Furthermore, long-range contextual dependencies and global low-frequency illumination properties inherent in hazing processes remain largely unexploited. In this paper, we propose a dehazing framework named DKS-Net which fully utilizes the physics guiding features and extracting structural information in the spatial domain. Motivated by the realization that the learning of information at multiple scales and frequency bands are important for the deep networks, we introduce a Kernel Selective Feature Extraction Module(KSFE) to effectively captures structural patterns via large-kernel convolutions with dynamic selection capabilities and multi-scale semantic cues. With the above techniques, our method can show state-of-the-art(SOTA) performance on synthetic datasets and real-world datasets, achieving competitive performance in visual quality.