In this paper,we propose Hformer,a novel supervised learning model for low-dose computer tomography(LDCT)denoising.Hformer combines the strengths of convolutional neural networks for local feature extraction and trans...In this paper,we propose Hformer,a novel supervised learning model for low-dose computer tomography(LDCT)denoising.Hformer combines the strengths of convolutional neural networks for local feature extraction and transformer models for global feature capture.The performance of Hformer was verified and evaluated based on the AAPM-Mayo Clinic LDCT Grand Challenge Dataset.Compared with the former representative state-of-the-art(SOTA)model designs under different architectures,Hformer achieved optimal metrics without requiring a large number of learning parameters,with metrics of33.4405 PSNR,8.6956 RMSE,and 0.9163 SSIM.The experiments demonstrated designed Hformer is a SOTA model for noise suppression,structure preservation,and lesion detection.展开更多
合成孔径雷达能够全天时,全天候产生高分辨率SAR图像。SAR图像中由于工作环境及成像机制会受到噪声影响,大多数去噪算法去除SAR图像噪声时会出现噪声去除不完全,图像信息损失的问题。针对这一问题,提出了一种基于U-Net网络结构改进的SA...合成孔径雷达能够全天时,全天候产生高分辨率SAR图像。SAR图像中由于工作环境及成像机制会受到噪声影响,大多数去噪算法去除SAR图像噪声时会出现噪声去除不完全,图像信息损失的问题。针对这一问题,提出了一种基于U-Net网络结构改进的SAR图像去噪算法。该算法采用VGG16网络结构作为特征提取模块,对SAR图像进行去噪的下采样操作,提取SAR图像中的关键特征,保留去噪后SAR图像的细节信息;采用修改的UNet上采样网络结构,让包含特征的低分辨率图片在保留特征的同时变为高分辨率,并通过特征融合使得去噪后SAR图像恢复更多细节,实现SAR图像的智能去噪。选择峰值信噪比(Peak Signal to Noise Ratio,PSNR)和结构相似性指数(Structural Similarity Index Measure,SSIM)作为实验的评价指标。仿真实验结果表明,该方法对添加噪声的SAR图像进行去噪,其主观视觉效果及客观评价指标PSNR和SSIM相比于实验对照去噪方法较高。所提方法兼顾了SAR图像噪点的去除和细节的保留,去噪获取的SAR图像具备更清晰的细节特征,具有较强的SAR图像去噪现实意义。展开更多
基金supported by the National Natural Science Foundation of China(Nos.11975292,12222512)the CAS"Light of West Chin"Program+1 种基金the CAS Pioneer Hundred Talent Programthe Guangdong Major Project of Basic and Applied Basic Research(No.2020B0301030008)。
文摘In this paper,we propose Hformer,a novel supervised learning model for low-dose computer tomography(LDCT)denoising.Hformer combines the strengths of convolutional neural networks for local feature extraction and transformer models for global feature capture.The performance of Hformer was verified and evaluated based on the AAPM-Mayo Clinic LDCT Grand Challenge Dataset.Compared with the former representative state-of-the-art(SOTA)model designs under different architectures,Hformer achieved optimal metrics without requiring a large number of learning parameters,with metrics of33.4405 PSNR,8.6956 RMSE,and 0.9163 SSIM.The experiments demonstrated designed Hformer is a SOTA model for noise suppression,structure preservation,and lesion detection.
文摘合成孔径雷达能够全天时,全天候产生高分辨率SAR图像。SAR图像中由于工作环境及成像机制会受到噪声影响,大多数去噪算法去除SAR图像噪声时会出现噪声去除不完全,图像信息损失的问题。针对这一问题,提出了一种基于U-Net网络结构改进的SAR图像去噪算法。该算法采用VGG16网络结构作为特征提取模块,对SAR图像进行去噪的下采样操作,提取SAR图像中的关键特征,保留去噪后SAR图像的细节信息;采用修改的UNet上采样网络结构,让包含特征的低分辨率图片在保留特征的同时变为高分辨率,并通过特征融合使得去噪后SAR图像恢复更多细节,实现SAR图像的智能去噪。选择峰值信噪比(Peak Signal to Noise Ratio,PSNR)和结构相似性指数(Structural Similarity Index Measure,SSIM)作为实验的评价指标。仿真实验结果表明,该方法对添加噪声的SAR图像进行去噪,其主观视觉效果及客观评价指标PSNR和SSIM相比于实验对照去噪方法较高。所提方法兼顾了SAR图像噪点的去除和细节的保留,去噪获取的SAR图像具备更清晰的细节特征,具有较强的SAR图像去噪现实意义。
