Aiming at the problems that the existing dehazing algorithms do not fully consider the fog information of haze image and the blurred details of the restored image,a novel haze feature map reflecting the distribution of fog information is proposed, and an inequality relation constraint method is adopted to enhance image quality.First,the extreme value channels of degraded image are extracted to achieve a rough estimation of fog information, and optimized by using L-1 regularization to obtain a haze feature map.After that,a primary atmospheric light veil function based on haze feature map is presented.Through in-depth analysis of color channel and atmospheric light veil, the constrained atmospheric light veil is obtained by using the mean value inequality.Finally,the local atmospheric light is improved by using the haze feature map,and haze removal is achieved based on the atmospheric scattering model.Compared with other existing state-of-the-art methods on both real-world and synthetic datasets haze images,our method shows favorable performance for single image dehazing especially in night image dehazing.