Joint image dehazing and denoising for single haze image enhancement

Abstract Outdoor haze images are typically degraded by noise due to the external environment and imaging equipment. The existing haze image enhancement methods ignore the interrelation between haze and noise, which cannot suppress the noise and remove the haze simultaneously. To address these intractable problems, a dual‐branch architecture that combines dehazing and denoising is proposed here to restore clear images. First, dark channel prior and unsupervised networks in the image dehazing branch to remove the image blur are adopted. Then, the image denoising branch removes the image noise in parallel by constructing a mean/extreme sampler and a self‐supervised network. Finally, a convolutional neural network fusion strategy is presented to fuse output images from the aforementioned two branches to generate the final qualified results. Extensive experiments reveal that the proposed haze image enhancement method outperforms other state‐of‐the‐art methods in terms of peak signal‐to‐noise ratio (PSNR) and structural similarity index measure (SSIM).