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

<p>By learning nonlinear mapping functions from low resolution (LR) images to high resolution (HR) images, deep neural networks show good performance in image super-resolution (SR). However, the existing SR approach has two potential limitations. First, learning the mapping function from LR to HR images is usually an ill-conditioned problem, since there exist an infinite number of HR images that can be down-sampled to the same LR image. Thus, the space of possible functions can be very large, making it difficult to find a good solution. Second, paired LR-HR data may not be available in real-world applications, and the underlying degradation method is often unknown. For this more general case, existing SR models tend to generate adaptive problems and produce poor performance. To solve the above problem, we propose a dual regression scheme that reduces the space of possible functions by introducing additional constraints on LR data.</p>
</abstract>

By learning nonlinear mapping functions from low resolution (LR) images to high resolution (HR) images, deep neural networks show good performance in image super-resolution (SR). However, the existing SR approach has two potential limitations. First, learning the mapping function from LR to HR images is usually an ill-conditioned problem, since there exist an infinite number of HR images that can be down-sampled to the same LR image. Thus, the space of possible functions can be very large, making it difficult to find a good solution. Second, paired LR-HR data may not be available in real-world applications, and the underlying degradation method is often unknown. For this more general case, existing SR models tend to generate adaptive problems and produce poor performance. To solve the above problem, we propose a dual regression scheme that reduces the space of possible functions by introducing additional constraints on LR data.

Super-resolution Image Reconstruction Based on Double Regression Network Model

School of Computer Science and Engineering

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":"Super-resolution Image Reconstruction Based on Double Regression Network Model"}

By learning nonlinear mapping functions from low resolution (LR) images to high resolution (HR) images, deep neural networks show good performance in image...

Super-resolution Image Reconstruction Based on Double Regression Network Model

Data publikacji: 26 maj 2023

Zakres stron: 82 - 88

DOI: https://doi.org/10.2478/ijanmc-2022-0039

Słowa kluczowe
Super Resolution, Mapping Function, Deep Neural Network, Double Regression Model

© 2022 Jieyi Lv et al., published by Sciendo

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

Super-resolution Image Reconstruction Based on Double Regression Network Model

Data publikacji: 26 maj 2023

Zakres stron: 82 - 88

DOI: https://doi.org/10.2478/ijanmc-2022-0039

Słowa kluczoweSuper Resolution, Mapping Function, Deep Neural Network, Double Regression Model

© 2022 Jieyi Lv et al., published by Sciendo

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

Słowa kluczowe
Super Resolution, Mapping Function, Deep Neural Network, Double Regression Model