SRResNet Performance Enhancement Using Patch Inputs and Partial Convolution-Based Padding-Reference-Cited by-同舟云学术

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SRResNet Performance Enhancement Using Patch Inputs and Partial Convolution-Based Padding

Published:2023 Issue:2 Volume:74 Page:2999-3014
ISSN:1546-2226
Container-title:Computers, Materials & Continua
language:en
Short-container-title:

Author:

Ullah Safi,Song Seong-Ho

Publisher

Computers, Materials and Continua (Tech Science Press)

Subject

Electrical and Electronic Engineering,Computer Science Applications,Mechanics of Materials,Modeling and Simulation,Biomaterials

Link

https://www.techscience.com/cmc/v74n2/50243/pdf

Reference42 articles.

1. GUN: Gradual upsampling network for single image super-resolution;Zhao;IEEE Access,2018

2. Learning from a handful volumes: MRI resolution enhancement with volumetric super-resolution forests;Sindal,2018

3. Depth estimation network for dual defocused images with different depth-of-field;Song,2018

4. Accurate image super-resolution using very deep convolutional networks;Kim,2016

5. Deeply-recursive convolutional network for image super-resolution;Kim,2016

Cited by 3 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Design of compensation algorithms for zero padding and its application to a patch based deep neural network;PeerJ Computer Science;2024-08-29

2. Image Quality Enhancement using Deep Convolutional Network;2024 International Conference on Inventive Computation Technologies (ICICT);2024-04-24

3. Review of dwell-time algorithms for magnetorheological finishing;The International Journal of Advanced Manufacturing Technology;2024-01-03

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