Because pixel values of foggy images are irregularly higher than those of images captured in normal weather(clear images),it is difficult to extract and express their texture.No method has previously been developed to...Because pixel values of foggy images are irregularly higher than those of images captured in normal weather(clear images),it is difficult to extract and express their texture.No method has previously been developed to directly explore the relationship between foggy images and semantic segmentation images.We investigated this relationship and propose a generative adversarial network(GAN)for foggy image semantic segmentation(FISS GAN),which contains two parts:an edge GAN and a semantic segmentation GAN.The edge GAN is designed to generate edge information from foggy images to provide auxiliary information to the semantic segmentation GAN.The semantic segmentation GAN is designed to extract and express the texture of foggy images and generate semantic segmentation images.Experiments on foggy cityscapes datasets and foggy driving datasets indicated that FISS GAN achieved state-of-the-art performance.展开更多
Focusing on the degradation of foggy images, a restora- tion approach from a single image based on spatial correlation of dark channel prior is proposed. Firstly, the transmission of each pixel is estimated by the spa...Focusing on the degradation of foggy images, a restora- tion approach from a single image based on spatial correlation of dark channel prior is proposed. Firstly, the transmission of each pixel is estimated by the spatial correlation of dark channel prior. Secondly, a degradation model is utilized to restore the foggy image. Thirdly, the final recovered image, with enhanced contrast, is obtained by performing a post-processing technique based on just-noticeable difference. Experimental results demonstrate that the information of a foggy image can be recovered perfectly by the proposed method, even in the case of the abrupt depth changing scene.展开更多
基金supported in part by the National Key Research and Development Program of China(2018YFB1305002)the National Natural Science Foundation of China(62006256)+2 种基金the Postdoctoral Science Foundation of China(2020M683050)the Key Research and Development Program of Guangzhou(202007050002)the Fundamental Research Funds for the Central Universities(67000-31610134)。
文摘Because pixel values of foggy images are irregularly higher than those of images captured in normal weather(clear images),it is difficult to extract and express their texture.No method has previously been developed to directly explore the relationship between foggy images and semantic segmentation images.We investigated this relationship and propose a generative adversarial network(GAN)for foggy image semantic segmentation(FISS GAN),which contains two parts:an edge GAN and a semantic segmentation GAN.The edge GAN is designed to generate edge information from foggy images to provide auxiliary information to the semantic segmentation GAN.The semantic segmentation GAN is designed to extract and express the texture of foggy images and generate semantic segmentation images.Experiments on foggy cityscapes datasets and foggy driving datasets indicated that FISS GAN achieved state-of-the-art performance.
基金supported by "the Twelfth Five-year Civil Aerospace Technologies Pre-Research Program"(D040201)
文摘Focusing on the degradation of foggy images, a restora- tion approach from a single image based on spatial correlation of dark channel prior is proposed. Firstly, the transmission of each pixel is estimated by the spatial correlation of dark channel prior. Secondly, a degradation model is utilized to restore the foggy image. Thirdly, the final recovered image, with enhanced contrast, is obtained by performing a post-processing technique based on just-noticeable difference. Experimental results demonstrate that the information of a foggy image can be recovered perfectly by the proposed method, even in the case of the abrupt depth changing scene.
