| Image translation is an important field in image processing of machine learning.Image translation refers to the corresponding transformation of two image domains with different characteristics,including style transformation,object transformation,seasonal transformation,image enhancement and so on.Traditionally,these tasks are handled separately according to the inherent differences between images of various styles and modes.In the past few years,common end-to-end deep learning frameworks,most notably full convolutional networks(FCNs)and conditional generation adversarial networks(CGANs),have promoted the development of image translation,enabling unified processing of multiple image translation problems.After the transformation from single-task image translation for specific domain to multi-task image translation for multi-domain,from the paired image set with labels to the unpaired image set with no label,the image translation process becomes simpler and more powerful.With regard to the problem of image translation between face photo and face sketches,a new network model was established by adding two loss functions to the objective function of the Dual GAN.Through optimization experiments of the parameters,the proposed model was continuously optimized to find the optimal parameters.The qualitative and quantitative comparison experiments show that the proposed model has excellent translation performance in face data in terms of sharpness and facial features,and it is now the best among the related GAN network models.The stability of related GAN models was then compared;finally,the effect analysis experiment clarified the specific function of the additional loss functions. |
