Multi-image hybrid super-resolution reconstruction via interpolation and multi-scale residual networks Article Swipe
Qiang Wu
,
Hongfei Zeng
,
Jin Zhang
,
Haojie Xia
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1088/1361-6501/accbdd
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1088/1361-6501/accbdd
High spatial resolution is necessary for several applications such as visual inspection, and can be achieved using high-resolution (HR) image sensors or through image super-resolution (SR) algorithms. Currently, SR algorithms are applied to either single low-resolution (LR) images or multiple LR image sequences. In this paper, we propose a hybrid super-resolution (HYSR) framework to generate HR images by combining multi-image super-resolution (MISR) and single-image super-resolution (SISR) to obtain high spatial resolution images. This method comprehensively utilizes sub-pixel-level high-frequency detail information between multiple images and co-occurrence prior of a single image to reconstruct SR images with a larger scale factor than the existing methods. Generally, the HYSR reconstruction results have more satisfactory details and visual quality than the SISR or MISR reconstruction results. A large number of qualitative and quantitative evaluation results demonstrate the effectiveness and superiority of the HYSR method over traditional MISR and SISR methods.
Related Topics
Concepts
Computer science
Interpolation (computer graphics)
Image resolution
Residual
Artificial intelligence
Image (mathematics)
Computer vision
Resolution (logic)
Scale (ratio)
Pixel
Iterative reconstruction
Sub-pixel resolution
Superresolution
Pattern recognition (psychology)
Algorithm
Image processing
Digital image processing
Quantum mechanics
Physics
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1088/1361-6501/accbdd
- OA Status
- hybrid
- Cited By
- 11
- References
- 32
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4364355953
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4364355953Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1088/1361-6501/accbddDigital Object Identifier
- Title
-
Multi-image hybrid super-resolution reconstruction via interpolation and multi-scale residual networksWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-04-11Full publication date if available
- Authors
-
Qiang Wu, Hongfei Zeng, Jin Zhang, Haojie XiaList of authors in order
- Landing page
-
https://doi.org/10.1088/1361-6501/accbddPublisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
hybridOpen access status per OpenAlex
- OA URL
-
https://doi.org/10.1088/1361-6501/accbddDirect OA link when available
- Concepts
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Computer science, Interpolation (computer graphics), Image resolution, Residual, Artificial intelligence, Image (mathematics), Computer vision, Resolution (logic), Scale (ratio), Pixel, Iterative reconstruction, Sub-pixel resolution, Superresolution, Pattern recognition (psychology), Algorithm, Image processing, Digital image processing, Quantum mechanics, PhysicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
11Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 3, 2024: 5, 2023: 3Per-year citation counts (last 5 years)
- References (count)
-
32Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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