LDPC-Net: A Lightweight Detail–Content Progressive Coupled Network for Single-Image Dehazing with Adaptive Feature Extraction Block

Abstract

Image dehazing is an effective means to enhance the quality of images captured in foggy or hazy weather conditions. However, the existing dehazing methods either cannot obtain satisfactory recovery results or have large model parameters. This limits the application of the model on resource-limited platforms. To overcome these limitations, we propose a lightweight yet effective image-dehazing method, named the lightweight detail–content progressive coupled network (LDPC-Net). Within the framework of LDPC-Net, we propose a progressive coupling dehazing paradigm. Specifically, we first estimate the details and content information of the haze-free image, and then fuse these estimations using the progressive coupling method. This proposed dehazing framework markedly enhances the operational efficiency of the model. Meanwhile, considering both the effectiveness and efficiency of the network, we also design a lightweight adaptive feature extraction block serving as the basic feature extraction module of the proposed LDPC-Net. Extensive experimental results demonstrate the effectiveness of our LDPC-Net, outperforming the state-of-the-art methods by boosting the PSNR index over 38.57 dB with only 0.708 M parameters.