Single Image Super Resolution Using Convolutional Neural Networks for Noisy Images

2020 ◽  
Author(s):  
Tae Bok Lee ◽  
Yong Seok Heo
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Super Resolution ◽  
Single Image ◽  
Noisy Images ◽  
Image Super Resolution ◽  
Single Image Super Resolution

scholarly journals Multi-Path Deep CNN with Residual Inception Network for Single Image Super-Resolution

Electronics ◽  
2021 ◽  
Vol 10 (16) ◽  
pp. 1979
Author(s):  
Wazir Muhammad ◽  
Zuhaibuddin Bhutto ◽  
Arslan Ansari ◽  
Mudasar Latif Memon ◽  
Ramesh Kumar ◽  
...  
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Computational Cost ◽  
Similarity Index ◽  
Super Resolution ◽  
Single Image ◽  
Index Matrix ◽  
Deep Cnn ◽  
Image Super Resolution ◽  
Single Image Super Resolution

Recent research on single-image super-resolution (SISR) using deep convolutional neural networks has made a breakthrough and achieved tremendous performance. Despite their significant progress, numerous convolutional neural networks (CNN) are limited in practical applications, owing to the requirement of the heavy computational cost of the model. This paper proposes a multi-path network for SISR, known as multi-path deep CNN with residual inception network for single image super-resolution. In detail, a residual/ResNet block with an Inception block supports the main framework of the entire network architecture. In addition, remove the batch normalization layer from the residual network (ResNet) block and max-pooling layer from the Inception block to further reduce the number of parameters to preventing the over-fitting problem during the training. Moreover, a conventional rectified linear unit (ReLU) is replaced with Leaky ReLU activation function to speed up the training process. Specifically, we propose a novel two upscale module, which adopts three paths to upscale the features by jointly using deconvolution and upsampling layers, instead of using single deconvolution layer or upsampling layer alone. The extensive experimental results on image super-resolution (SR) using five publicly available test datasets, which show that the proposed model not only attains the higher score of peak signal-to-noise ratio/structural similarity index matrix (PSNR/SSIM) but also enables faster and more efficient calculations against the existing image SR methods. For instance, we improved our method in terms of overall PSNR on the SET5 dataset with challenging upscale factor 8× as 1.88 dB over the baseline bicubic method and reduced computational cost in terms of number of parameters 62% by deeply-recursive convolutional neural network (DRCN) method.

scholarly journals Single-Image Super-Resolution of Sentinel-2 Low Resolution Bands with Residual Dense Convolutional Neural Networks

2021 ◽  
Vol 13 (24) ◽  
pp. 5007
Author(s):  
Luis Salgueiro ◽  
Javier Marcello ◽  
Verónica Vilaplana
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Super Resolution ◽  
Superior Performance ◽  
Single Image ◽  
Low Resolution ◽  
Spatial Coverage ◽  
Image Super Resolution ◽  
Single Image Super Resolution ◽  
Sentinel 2

Sentinel-2 satellites have become one of the main resources for Earth observation images because they are free of charge, have a great spatial coverage and high temporal revisit. Sentinel-2 senses the same location providing different spatial resolutions as well as generating a multi-spectral image with 13 bands of 10, 20, and 60 m/pixel. In this work, we propose a single-image super-resolution model based on convolutional neural networks that enhances the low-resolution bands (20 m and 60 m) to reach the maximal resolution sensed (10 m) at the same time, whereas other approaches provide two independent models for each group of LR bands. Our proposed model, named Sen2-RDSR, is made up of Residual in Residual blocks that produce two final outputs at maximal resolution, one for 20 m/pixel bands and the other for 60 m/pixel bands. The training is done in two stages, first focusing on 20 m bands and then on the 60 m bands. Experimental results using six quality metrics (RMSE, SRE, SAM, PSNR, SSIM, ERGAS) show that our model has superior performance compared to other state-of-the-art approaches, and it is very effective and suitable as a preliminary step for land and coastal applications, as studies involving pixel-based classification for Land-Use-Land-Cover or the generation of vegetation indices.

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