Due to the influence of hazy weather, the images captured often suffer from severe degradation, which will reduce the effectiveness of the devices for computer-related applications. To eliminate the degradation of hazy images, a multiscale image dehazing network based on spatially weighted sparse representation is proposed. The model is mainly divided into two modules: multiscale module (MSM) and detail enhancement module (DEM). The MSM can effectively obtain more feature information in more hazy images. Specifically, the degradation of hazy image is considered in terms of haze distribution and image details. A spatially weighted nonlocal sparse attention module was developed to focus on channel-level features adaptively and to pay more attention to the recovery of dense haze areas in response to the problem of uneven distribution of haze in images. In addition, an independent feature fusion module is proposed to capture better the contextual information, which can also effectively address the traditional fusion, leading to redundant feature extraction and hindering the reconstructive performance of the network. Finally, a DEM is employed by a combination of pyramid pooling and resblock group, which guides the adaptive fusion of latent features, where different details and plausible appearances are integrated. Experimental results on public datasets show that this method is effective and superior in the metrics of structure similarity (SSIM), peak-signal-to-noise ratio, multiscale-SSIM, and root-mean-square error.