Abstract: To address the problem of significantly degraded image quality in hazy weather, a new multi-task learning method is proposed based on the classical atmospheric scattering model. This method aims to jointly learn the transmission map and dehazed image in an end-to-end manner. The network framework is built upon a new biconditional generative adversarial network, which consists of two improved CGANs( Conditional Generative Adversarial Network). The hazy image is inputted into the first stage CGAN to estimate the transmission map. Then, the predicted transmission map and the hazy image are passed into the second stage CGAN, which generates the corresponding dehazed image. To improve the color distortion and edge blurring in the output image, a joint loss function is designed to enhance the quality of image transformation. By conducting qualitative and quantitative experiments on synthetic and real datasets, and comparing with various dehazing methods, the results demonstrate that the dehazed images produced by this method exhibit better visual effects. The structural similarity index is measured at 0. 985, and the peak signal-to-noise ratio value is 32. 880 dB.

Key words: image dehazing, atmospheric scattering model, conditional generative adversarial network, multi- task learning, joint loss