Super Resolution Recurrent Convolutional Neural Networks For Learning We propose a recurrent convolutional neural network model, to generate sr images from such multi resolution wsi datasets. We propose convo lutional neural network(cnn) based approach and its advanced recurrent version(rcnn) to solve the problem of enhancing the resolution of images obtained from a low magnification scanner, also known as the image super resolution (sr) problem.
Super Resolution Recurrent Convolutional Neural Networks For Learning Motivated by this concern, in this paper, we propose a cascaded convolution neural network for image super resolution (csrcnn), which includes three cascaded fast srcnns and each fast srcnn can process a specific scale image. In this paper, we present a deformable convolutional neural network for video super resolution (dvsrnet). dvsrnet mainly contains forward and backward feature propagation blocks (fpbs), feature enhancement blocks (febs), a feature fusion block (ffb), and a reconstruction block (rb). In this paper, we propose a progressive cascaded recurrent convolutional network, named pcrcn, for low quality face super resolution with high magnification factor. Limet al.[25] proposed the enhanced deep super resolution (edsr) network and a multi scale vari ant, which learns different scaled mapping functions in par allel via weight sharing.
Super Resolution Recurrent Convolutional Neural Networks For Learning In this paper, we propose a progressive cascaded recurrent convolutional network, named pcrcn, for low quality face super resolution with high magnification factor. Limet al.[25] proposed the enhanced deep super resolution (edsr) network and a multi scale vari ant, which learns different scaled mapping functions in par allel via weight sharing. This work proposes a bidirectional recurrent convolutional network for efficient multi frame sr, different from vanilla rnns, which has a low computational complexity and runs orders of magnitude faster than other multi frame methods. We propose a bidirectional recurrent con volutional network for multi frame sr, where the temporal dependency can be efficiently modelled by bidirectional recurrent and conditional convolutions. it is an end to end framework which does not need pre post processing. In this paper, we explore the use of rnns for dealing with the temporal dependency in video sr. a straightforward approach in this direction is to treat given low resolution frames as sequential inputs to a rnn, and then regard its outputs at different timesteps as predicted high resolution frames. In this study, we bring forward a unique lightweight model for video super resolution, the deep residual recursive network (drrn). the model applies residual learning to stabilize the recurrent neural network (rnn) training, meanwhile adopting depth wise separable convolution to boost the efficiency of super resolution operations.
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