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 in this paper to restore clear images. First, we adopt dark channel prior and unsupervised networks in the image dehazing branch to remove the image blur. Then, the image denoising branch removes the image noise in parallel by constructing a mean/extreme sampler and a self-supervised network. Finally, a CNN 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 PSNR and SSIM.