Super-Resolution Reconstruction Algorithm for Infrared Image with Double Regular Items Based on Sub-Pixel Convolution
Lei Yu,Xuewei Zhang,Yan Chu +2 more
TL;DR: An adaptive dual-regularization super-resolution reconstruction algorithm based on sub-pixel convolution (MPSR) based on which the subjective and objective evaluation indexes (PSNR/SSIM) of the algorithm have achieved satisfactory results.
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Abstract: In this paper, an adaptive dual-regularization super-resolution reconstruction algorithm based on sub-pixel convolution (MPSR) is proposed. There are two novel features of the algorithm: First, the traditional regularization algorithm and sub-pixel convolution algorithm are combined to enrich the details; then, a regularization function with two adaptive parameters and two regularization terms is proposed to enhance the edge. MPSR firstly enhances the multi-scale detail of low-resolution images; then, regular processing and feature extraction are carried out; finally, sub-pixel convolution is used to fuse the extracted features to generate high-resolution images. The experimental results show that the subjective and objective evaluation indexes (PSNR/SSIM) of the algorithm have achieved satisfactory results.
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References
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
Christian Ledig,Lucas Theis,Ferenc Huszar,Jose Caballero,Andrew Cunningham,Alejandro Acosta,Andrew Peter Aitken,Alykhan Tejani,Johannes Totz,Zehan Wang,Wenzhe Shi +10 more
- 21 Jul 2017
TL;DR: SRGAN as mentioned in this paper proposes a perceptual loss function which consists of an adversarial loss and a content loss, which pushes the solution to the natural image manifold using a discriminator network that is trained to differentiate between the super-resolved images and original photo-realistic images.
Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network
Wenzhe Shi,Jose Caballero,Ferenc Huszar,Johannes Totz,Andrew Peter Aitken,Rob Bishop,Daniel Rueckert,Zehan Wang +7 more
- 27 Jun 2016
TL;DR: This paper presents the first convolutional neural network capable of real-time SR of 1080p videos on a single K2 GPU and introduces an efficient sub-pixel convolution layer which learns an array of upscaling filters to upscale the final LR feature maps into the HR output.
Learning a Deep Convolutional Network for Image Super-Resolution
Chao Dong,Chen Change Loy,Kaiming He,Xiaoou Tang +3 more
- 06 Sep 2014
TL;DR: This work proposes a deep learning method for single image super-resolution (SR) that directly learns an end-to-end mapping between the low/high-resolution images and shows that traditional sparse-coding-based SR methods can also be viewed as a deep convolutional network.
Residual Dense Network for Image Super-Resolution
Yulun Zhang,Yapeng Tian,Yu Kong,Bineng Zhong,Yun Fu +4 more
- 18 Jun 2018
TL;DR: This paper proposes residual dense block (RDB) to extract abundant local features via dense connected convolutional layers and uses global feature fusion in RDB to jointly and adaptively learn global hierarchical features in a holistic way.
Accelerating the Super-Resolution Convolutional Neural Network
Chao Dong,Chen Change Loy,Xiaoou Tang +2 more
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TL;DR: Zhang et al. as mentioned in this paper proposed a compact hourglass-shape CNN structure for faster and better image super-resolution, which can achieve real-time performance on a generic CPU while still maintaining good performance.