degradation problem不是网络结构本身的问题,而是现有的训练方式不够理想造成的。 递归学习 Recursive Learning 将某些模块重复多次,从而不会引入大量参数。优点是不需引入大量的新参数,可以学得更advanced representations。缺点是仍不能避免high computational costs,并且会带来梯度消失/爆炸的问题。 多路径学习 Multi-path ...
图像超分辨率(SR)是指从低分辨率(LR)图像中恢复高分辨率(HR)图像的过程,是计算机视觉和图像处理中一类重要的图像处理技术。它具有广泛的现实应用,例如医学成像、监控和安全等。除了提高图像感知质量外,它还有助于改进其他计算机视觉任务。一般来说,这个问题非常具有挑战性,并且本质上不适定,因为总是有多个HR图像对应...
通过不同角度的旋转与水平翻转,得到8张LR的图像集合,然后将这些图像丢进SR模型,输出的结果再做相应的操作得到正确位置的HR,最终将这8张SR进行平均/或者输出中位数。 4.Unsupervised Super-Resolution 现存的SR大多是学习LR-HR的映射 LR通常是通过SR预定义的退化得来的。因此,SR模型学习的相当于是一个反退化的过程。
使用深度学习的超分辨率介绍 An Introduction to Super Resolution using Deep Learning 使用深度学习的超分辨率介绍 关于使用深度学习进行超分辨率的各种组件,损失函数和度量的详细讨论。 介绍 超分辨率是从给定的低分辨率(LR)图像恢复高分辨率(HR)图像的过程。由于较小的空间分辨率(即尺寸)或由于退化的结果(例如模糊),...
A system and method to use deep learning for super resolution in a radar system include obtaining first-resolution time samples from reflections based on transmissions by a first-resolution radar system of multiple frequency-modulated signals. The first-resolution radar system includes multiple transmit...
内容提示: 1Deep Learning for Image Super-resolution:A SurveyZhihao Wang, Jian Chen, Steven C.H. Hoi, Fellow, IEEEAbstract—Image Super-Resolution (SR) is an important class of image processing techniques to enhance the resolution of imagesand videos in computer vision. Recent years have ...
For the general reason that results often get better as DNN becomes deeper, we believe that a much deeper structure may boost the performance of DNN based DOA estimation. In this paper, we take advantage of deep learning techniques to boost the resolution and generalization of DNN based DOA ...
How- ever, acquiring quantitative biomarkers requires high signal-to-noise ratio (SNR), which is at odds with high-resolution in MRI, especially in a single rapid sequence. In this paper, we demonstrate how super-resolution can be utilized to maintain adequate SNR for accurate quantification of...
Image Super-Resolution (SR) is an important class of image processing techniques to enhance the resolution of images and videos in computer vision. Recent years have witnessed remarkable progress of image super-resolution using deep learning techniques. This article aims to provide a comprehensive surv...
We present deep-learning-enabled super-resolution across different fluorescence microscopy modalities. This data-driven approach does not require numerical modeling of the imaging process or the estimation of a point-spread-function, and is based on training a generative adversarial network (GAN) to tr...