IPDNet: A dual convolutional network combined with image prior for single image dehazing

Dehazing based on deep learning neural networks (CNNs) has achieved remarkable results. However, the most existing dehazing CNNs perform well only on synthetic images and struggle with realistic hazy images. Moreover, training complex dehazing models is challenging due to the limited availability of realistic image datasets, leading to suboptimal performance. For this purpose, we develop a novel Image Prior Dehazing Network called IPDNet to tackle the challenge of dehazing realistic images with limited training data. The IPDNet comprises two sub-networks and a learnable fusion block. The global and local features are obtained by atmospheric scattering and direct mapping via two sub-networks with a sparse mechanism. And the learnable fusion block is settled to acquire the optimal fusion solution to improve the dehazing quality. The IPDNet offers several benefits: (1) a dual network with a learnable fusion block can effectively generate high-quality dehazed images at low computational cost; (2) an image preprocessing block based on dehazing prior can acquires the salient features of realistic hazy image, enhancing the dehazing performance on realistic hazy images; (3) the sub-network is designed by incorporating a feature attention (FA) block into the U-net structure, allowing flexible feature extraction on limited datasets. Extensive experiments on SOTS, NH-HAZE, and DENSE-HAZE datasets show that IPDNet outperforms other state-of-the-art methods on synthetic and realistic datasets. Specifically, our model achieved an improvement of approximately 0.84 dB in PSNR and 11.6% in SSIM, demonstrating its effectiveness in realistic scenarios, which contributes to improving traffic safety in adverse weather conditions.