paper_authors: Fanwen Wang, Pedro F. Ferreira, Yinzhe Wu, Andrew D. Scott, Camila Munoz, Ke Wen, Yaqing Luo, Jiahao Huang, Sonia Nielles-Vallespin, Dudley J. Pennell, Guang Yang
For: 提供非侵入式的心肺功能测量方法* Methods: 使用深度学习基于B-spline网络对DT-CMR图像进行抗干扰注射注射的抽象注射注射* Results: 提高了图像使用效率、手动剪辑和计算速度Abstract
Diffusion tensor based cardiac magnetic resonance (DT-CMR) is a method capable of providing non-invasive measurements of myocardial microstructure. Image registration is essential to correct image shifts due to intra and inter breath-hold motion. Registration is challenging in DT-CMR due to the low signal-to-noise and various contrasts induced by the diffusion encoding in the myocardial and surrounding organs. Traditional deformable registration destroys the texture information while rigid registration inefficiently discards frames with local deformation. In this study, we explored the possibility of deep learning-based deformable registration on DT- CMR. Based on the noise suppression using low-rank features and diffusion encoding suppression using variational auto encoder-decoder, a B-spline based registration network extracted the displacement fields and maintained the texture features of DT-CMR. In this way, our method improved the efficiency of frame utilization, manual cropping, and computational speed.
摘要
Diffusion tensor based cardiac magnetic resonance (DT-CMR) 是一种可以提供非侵入式的肌肉微结构测量方法。图像匹配是必要的，以正确补做图像偏移 Due to intra-和inter- breath-hold 运动。但是，在 DT-CMR 中，匹配是具有挑战性，因为Diffusion encoding在肌肉和周围的器官中induced的低信号至噪声和多种对比。传统的可变形注册会destroys the texture information，而rigid注册则不 efficiently discards frames with local deformation。在这种研究中，我们探索了深度学习基于的可变形注册方法在 DT-CMR 中。基于噪声抑制使用低级特征和Diffusion encoding抑制使用自适应变换器-解码器，一个基于 B-spline 的注册网络提取出了displacement fields 并保留了 DT-CMR 中的 texture features。这种方法可以提高帧使用效率、手动剪辑和计算速度。
results: 比州前方法提高1.2dB至1.8dB的PSNR值，可见地提高图像的分辨率Abstract
This paper introduces a novel method for RGB-Guided Resolution Enhancement of infrared (IR) images called Guided IR Resolution Enhancement (GIRRE). In the area of single image super resolution (SISR) there exists a wide variety of algorithms like interpolation methods or neural networks to improve the spatial resolution of images. In contrast to SISR, even more information can be gathered on the recorded scene when using multiple cameras. In our setup, we are dealing with multi image super resolution, especially with stereo super resolution. We consider a color camera and an IR camera. Current IR sensors have a very low resolution compared to color sensors so that recent color sensors take up 100 times more pixels than IR sensors. To this end, GIRRE increases the spatial resolution of the low-resolution IR image. After that, the upscaled image is filtered with the aid of the high-resolution color image. We show that our method achieves an average PSNR gain of 1.2 dB and at best up to 1.8 dB compared to state-of-the-art methods, which is visually noticeable.
摘要
Here is the Simplified Chinese translation of the text:这篇论文提出了一种基于RGB指导的红外图像分辨率提高方法，称为指导红外分辨率提高（GIRRE）。与传统单张图像超解（SISR）方法不同的是，GIRRE利用多个摄像头来提高低分辨率红外图像的空间分辨率。具体来说，我们使用了一个颜色摄像头和一个红外摄像头，高分辨率颜色图像用于指导低分辨率红外图像的扩展。我们的方法实现了平均PSNR提升1.2dB，最高可达1.8dB compared to state-of-the-art方法，可见程度有所提高。