Fusion Objective Function on Progressive Super-Resolution Network Article Swipe
Amir Hajian
,
Supavadee Aramvith
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.3390/jsan12020026
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.3390/jsan12020026
Recent advancements in Single-Image Super-Resolution (SISR) have explored the network architecture of deep-learning models to achieve a better perceptual quality of super-resolved images. However, the effect of the objective function, which contributes to improving the performance and perceptual quality of super-resolved images, has not gained much attention. This paper proposes a novel super-resolution architecture called Progressive Multi-Residual Fusion Network (PMRF), which fuses the learning objective functions of L2 and Multi-Scale SSIM in a progressively upsampling framework structure. Specifically, we propose a Residual-in-Residual Dense Blocks (RRDB) architecture on a progressively upsampling platform that reconstructs the high-resolution image during intermediate steps in our super-resolution network. Additionally, the Depth-Wise Bottleneck Projection allows high-frequency information of early network layers to be bypassed through the upsampling modules of the network. Quantitative and qualitative evaluation of benchmark datasets demonstrate that the proposed PMRF super-resolution algorithm with novel fusion objective function (L2 and MS-SSIM) improves our model’s perceptual quality and accuracy compared to other state-of-the-art models. Moreover, this model demonstrates robustness against noise degradation and achieves an acceptable trade-off between network efficiency and accuracy.
Related Topics
Concepts
Upsampling
Computer science
Residual
Robustness (evolution)
Artificial intelligence
Benchmark (surveying)
Network architecture
Fusion
Image quality
Superresolution
Pattern recognition (psychology)
Computer vision
Image (mathematics)
Algorithm
Computer security
Gene
Biochemistry
Chemistry
Philosophy
Geography
Linguistics
Geodesy
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/jsan12020026
- https://www.mdpi.com/2224-2708/12/2/26/pdf?version=1679299992
- OA Status
- gold
- Cited By
- 9
- References
- 61
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4327955583
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4327955583Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3390/jsan12020026Digital Object Identifier
- Title
-
Fusion Objective Function on Progressive Super-Resolution NetworkWork title
- Type
-
articleOpenAlex work type
- Language
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enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-03-20Full publication date if available
- Authors
-
Amir Hajian, Supavadee AramvithList of authors in order
- Landing page
-
https://doi.org/10.3390/jsan12020026Publisher landing page
- PDF URL
-
https://www.mdpi.com/2224-2708/12/2/26/pdf?version=1679299992Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
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https://www.mdpi.com/2224-2708/12/2/26/pdf?version=1679299992Direct OA link when available
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Upsampling, Computer science, Residual, Robustness (evolution), Artificial intelligence, Benchmark (surveying), Network architecture, Fusion, Image quality, Superresolution, Pattern recognition (psychology), Computer vision, Image (mathematics), Algorithm, Computer security, Gene, Biochemistry, Chemistry, Philosophy, Geography, Linguistics, GeodesyTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
9Total citation count in OpenAlex
- Citations by year (recent)
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2025: 2, 2024: 5, 2023: 2Per-year citation counts (last 5 years)
- References (count)
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61Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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