In this paper,we propose a framework based deep learning for medical image translation using paired and unpaired training data.Initially,a deep neural network with an encoder-decoder structure is proposed for image-to...In this paper,we propose a framework based deep learning for medical image translation using paired and unpaired training data.Initially,a deep neural network with an encoder-decoder structure is proposed for image-to-image translation using paired training data.A multi-scale context aggregation approach is then used to extract various features from different levels of encoding,which are used during the corresponding network decoding stage.At this point,we further propose an edge-guided generative adversarial network for image-to-image translation based on unpaired training data.An edge constraint loss function is used to improve network performance in tissue boundaries.To analyze framework performance,we conducted five different medical image translation tasks.The assessment demonstrates that the proposed deep learning framework brings significant improvement beyond state-of-the-arts.展开更多
Recovering high-quality inscription images from unknown and complex inscription noisy images is a challenging research issue.Different fromnatural images,character images pay more attention to stroke information.Howev...Recovering high-quality inscription images from unknown and complex inscription noisy images is a challenging research issue.Different fromnatural images,character images pay more attention to stroke information.However,existingmodelsmainly consider pixel-level informationwhile ignoring structural information of the character,such as its edge and glyph,resulting in reconstructed images with mottled local structure and character damage.To solve these problems,we propose a novel generative adversarial network(GAN)framework based on an edge-guided generator and a discriminator constructed by a dual-domain U-Net framework,i.e.,EDU-GAN.Unlike existing frameworks,the generator introduces the edge extractionmodule,guiding it into the denoising process through the attention mechanism,which maintains the edge detail of the restored inscription image.Moreover,a dual-domain U-Net-based discriminator is proposed to learn the global and local discrepancy between the denoised and the label images in both image and morphological domains,which is helpful to blind denoising tasks.The proposed dual-domain discriminator and generator for adversarial training can reduce local artifacts and keep the denoised character structure intact.Due to the lack of a real-inscription image,we built the real-inscription dataset to provide an effective benchmark for studying inscription image denoising.The experimental results show the superiority of our method both in the synthetic and real-inscription datasets.展开更多
文摘In this paper,we propose a framework based deep learning for medical image translation using paired and unpaired training data.Initially,a deep neural network with an encoder-decoder structure is proposed for image-to-image translation using paired training data.A multi-scale context aggregation approach is then used to extract various features from different levels of encoding,which are used during the corresponding network decoding stage.At this point,we further propose an edge-guided generative adversarial network for image-to-image translation based on unpaired training data.An edge constraint loss function is used to improve network performance in tissue boundaries.To analyze framework performance,we conducted five different medical image translation tasks.The assessment demonstrates that the proposed deep learning framework brings significant improvement beyond state-of-the-arts.
基金supported by the Key R&D Program of Shaanxi Province,China(Grant Nos.2022GY-274,2023-YBSF-505)the National Natural Science Foundation of China(Grant No.62273273).
文摘Recovering high-quality inscription images from unknown and complex inscription noisy images is a challenging research issue.Different fromnatural images,character images pay more attention to stroke information.However,existingmodelsmainly consider pixel-level informationwhile ignoring structural information of the character,such as its edge and glyph,resulting in reconstructed images with mottled local structure and character damage.To solve these problems,we propose a novel generative adversarial network(GAN)framework based on an edge-guided generator and a discriminator constructed by a dual-domain U-Net framework,i.e.,EDU-GAN.Unlike existing frameworks,the generator introduces the edge extractionmodule,guiding it into the denoising process through the attention mechanism,which maintains the edge detail of the restored inscription image.Moreover,a dual-domain U-Net-based discriminator is proposed to learn the global and local discrepancy between the denoised and the label images in both image and morphological domains,which is helpful to blind denoising tasks.The proposed dual-domain discriminator and generator for adversarial training can reduce local artifacts and keep the denoised character structure intact.Due to the lack of a real-inscription image,we built the real-inscription dataset to provide an effective benchmark for studying inscription image denoising.The experimental results show the superiority of our method both in the synthetic and real-inscription datasets.
