GUN: Gradual Upsampling Network for single image super-resolution

03/13/2017
by   fcq, et al.
0

In this paper, we propose an efficient super-resolution (SR) method based on deep convolutional neural network (CNN), namely gradual upsampling network (GUN). Recent CNN based SR methods either preliminarily magnify the low resolution (LR) input to high resolution (HR) and then reconstruct the HR input, or directly reconstruct the LR input and then recover the HR result at the last layer. The proposed GUN utilizes a gradual process instead of these two kinds of frameworks. The GUN consists of an input layer, multistep upsampling and convolutional layers, and an output layer. By means of the gradual process, the proposed network can simplify the difficult direct SR problem to multistep easier upsampling tasks with very small magnification factor in each step. Furthermore, a gradual training strategy is presented for the GUN. In the proposed training process, an initial network can be easily trained with edge-like samples, and then the weights are gradually tuned with more complex samples. The GUN can recover fine and vivid results, and is easy to be trained. The experimental results on several image sets demonstrate the effectiveness of the proposed network.

READ FULL TEXT

page 3

page 6

page 8

research
11/11/2017

CT-SRCNN: Cascade Trained and Trimmed Deep Convolutional Neural Networks for Image Super Resolution

We propose methodologies to train highly accurate and efficient deep con...
research
12/31/2014

Image Super-Resolution Using Deep Convolutional Networks

We propose a deep learning method for single image super-resolution (SR)...
research
03/12/2017

Local Patch Classification Based Framework for Single Image Super-Resolution

Recent learning-based super-resolution (SR) methods often focus on the d...
research
03/22/2019

Fast Bayesian Uncertainty Estimation of Batch Normalized Single Image Super-Resolution Network

In recent years, deep convolutional neural network (CNN) has achieved un...
research
05/03/2023

Bicubic++: Slim, Slimmer, Slimmest – Designing an Industry-Grade Super-Resolution Network

We propose a real-time and lightweight single-image super-resolution (SR...
research
07/22/2019

Learned Image Downscaling for Upscaling using Content Adaptive Resampler

Deep convolutional neural network based image super-resolution (SR) mode...
research
04/21/2021

SRWarp: Generalized Image Super-Resolution under Arbitrary Transformation

Deep CNNs have achieved significant successes in image processing and it...

Please sign up or login with your details

Forgot password? Click here to reset