A scene restoration algorithm based on image fusion and segmentation is proposed to enhance the contrast and detail information of haze images captured by a machine vision system. Haze density is roughly estimated based on the physical properties of the optical reflectance imaging and morphology operation. The atmospheric veil is then estimated accurately by using weighted image fusion and by computing for the local variance. The global atmospheric light is obtained by segmenting the most hazed region or the sky part of the image. Finally

the ideal result is obtained through a physical model

and the brightness and chroma of the images are adjusted via tone mapping. This method can avoid halo artifacts or color distortion while achieving a good restoration of contrast and color fidelity. Results show that the proposed method has robust scene adaptability and achieves different degrees of improvement in terms of restoration effect and computation speed.