In terms of the problems such as haze residues and color distortion in existing dehazing methods, this study takes advantage of a generative adversarial network in reconstructing image super-resolution and proposes an image dehazing algorithm based on channel attention and conditional generative adversarial network (CGAN-ECA). Specifically, the network is based on the encoder-decoder structure. The generator is designed with the multi-scale residual block (MRBlk) and efficient channel attention (ECA) to expand the receptive field, extract multi-scale features, dynamically adjust the weights of different channels, and improve the utilization rate of features. In addition, the Markovian discriminator (PatchGAN) is used to evaluate images and improve the accuracy in identifying images. At the same time, a content loss is added into the loss function to reduce pixel-level and feature-level losses of dehazing images, retain more image details, and achieve high-quality image dehazing. The test results based on the public dataset RESIDE show that compared with DCP, AOD-Net, DehazeNet, and GCANet models, the proposed model increases the peak signal to noise ratio (PSNR) and the structural similarity index (SSIM) by 36.36% and 8.80%, respectively, and color distortion and haze residue are solved. Therefore, CGAN-ECA is an effective method for image dehazing.