<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>The existing super resolution reconstruction methods are mainly divided into traditional super resolution reconstruction and deep learning super resolution reconstruction. The main problem faced by traditional super resolution reconstruction algorithms, such as image enlargement and space transformation, is how to establish the mapping relationship between the input image and the target image, and express the pixel value of the target image through the mapping relationship. As a prominent problem, the difficulty of super resolution reconstruction lies in the fact that there is no realizable matrix relationship between one - to - many mapping relationships. Based on the U-Net network framework, this paper improves the jump-connected modules. By using the combination of convolutional layer, activation layer and residual channel block, the overall module operation efficiency is increased by 2.4%, the overall PNSR is increased by 0.49db, and the running speed is increased by 0.3ms on average when processing a single image compared with other classical models.</p>
</abstract>

The existing super resolution reconstruction methods are mainly divided into traditional super resolution reconstruction and deep learning super resolution reconstruction. The main problem faced by traditional super resolution reconstruction algorithms, such as image enlargement and space transformation, is how to establish the mapping relationship between the input image and the target image, and express the pixel value of the target image through the mapping relationship. As a prominent problem, the difficulty of super resolution reconstruction lies in the fact that there is no realizable matrix relationship between one - to - many mapping relationships. Based on the U-Net network framework, this paper improves the jump-connected modules. By using the combination of convolutional layer, activation layer and residual channel block, the overall module operation efficiency is increased by 2.4%, the overall PNSR is increased by 0.49db, and the running speed is increased by 0.3ms on average when processing a single image compared with other classical models.

Improved Double Regression Nonlinear Image Super Resolution Model

State and Provincial Joint Engineering Lab. of Advanced Network, Monitoring and Control

International Journal of Advanced Network, Monitoring and Controls

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

{"article-title":"Improved Double Regression Nonlinear Image Super Resolution Model"}

The existing super resolution reconstruction methods are mainly divided into traditional super resolution reconstruction and deep learning super resolution...

Improved Double Regression Nonlinear Image Super Resolution Model

Pubblicato online: 16 ago 2023

Pagine: 46 - 53

DOI: https://doi.org/10.2478/ijanmc-2023-0055

Parole chiave
Super-resolution, Double Regression, U-Net network, Model Refinement

© 2023 Jieyi Lv et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Improved Double Regression Nonlinear Image Super Resolution Model

Pubblicato online: 16 ago 2023

Pagine: 46 - 53

DOI: https://doi.org/10.2478/ijanmc-2023-0055

Parole chiaveSuper-resolution, Double Regression, U-Net network, Model Refinement

© 2023 Jieyi Lv et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Parole chiave
Super-resolution, Double Regression, U-Net network, Model Refinement