Improved Super-Resolution Convolution Neural Network for Large Images

07/26/2019
by   Junyu, et al.
7

Single image super-resolution (SISR) is a very popular topic nowadays, which has both research value and practical value. In daily life, we crop a large image into sub-images to do super-resolution and then merge them together. Although convolution neural network performs very well in the research field, if we use it to do super-resolution, we can easily observe cutting lines from merged pictures. To address these problems, in this paper, we propose a refined architecture of SRCNN with 'Symmetric padding', 'Random learning' and 'Residual learning'. Moreover, we have done a lot of experiments to prove our model performs best among a lot of the state-of-art methods.

READ FULL TEXT

page 2

page 6

page 10

research
01/16/2022

CISRNet: Compressed Image Super-Resolution Network

In recent years, tons of research has been conducted on Single Image Sup...
research
08/07/2019

Linear Depthwise Convolution for Single Image Super-Resolution

Recent work on super-resolution show that a very deep convolutional neur...
research
02/22/2022

Convolutional Neural Network Modelling for MODIS Land Surface Temperature Super-Resolution

Nowadays, thermal infrared satellite remote sensors enable to extract ve...
research
07/24/2021

LAConv: Local Adaptive Convolution for Image Fusion

The convolution operation is a powerful tool for feature extraction and ...
research
07/22/2023

NLCUnet: Single-Image Super-Resolution Network with Hairline Details

Pursuing the precise details of super-resolution images is challenging f...
research
12/06/2018

Binary Document Image Super Resolution for Improved Readability and OCR Performance

There is a need for information retrieval from large collections of low-...
research
03/08/2020

Perceptual Image Super-Resolution with Progressive Adversarial Network

Single Image Super-Resolution (SISR) aims to improve resolution of small...

Please sign up or login with your details

Forgot password? Click here to reset