Herein, a dehazing algorithm based on a full convolution regression network is proposed to solve the overexposure and color distortions caused by current dehazing algorithms. The regression network is based on an end-to-end system and comprises two parts, feature extraction and feature fusion, to which a foggy image is first subjected, then regressed into a coarse transmittance map and optimized by the guide filter. The atmospheric physical scattering model is used to invert a fog-free image , which is then enhanced via contrast limit adaptive histogram equalization (CLAHE) to obtain a clear image that is more suitable to human vision. The proposed algorithm can avoid problems such as overexposure and color distortion post dehazing, retain complete details, and provide a better dehazing effect.