Low-Resource Video Super-Resolution using Memory, Wavelets, and Deformable Convolutions Article Swipe
Kavitha Viswanathan
,
Shashwat Pathak
,
Piyush Bharambe
,
Harsh Choudhary
,
Amit Sethi
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2502.01816
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2502.01816
The tradeoff between reconstruction quality and compute required for video super-resolution (VSR) remains a formidable challenge in its adoption for deployment on resource-constrained edge devices. While transformer-based VSR models have set new benchmarks for reconstruction quality in recent years, these require substantial computational resources. On the other hand, lightweight models that have been introduced even recently struggle to deliver state-of-the-art reconstruction. We propose a novel lightweight and parameter-efficient neural architecture for VSR that achieves state-of-the-art reconstruction accuracy with just 2.3 million parameters. Our model enhances information utilization based on several architectural attributes. Firstly, it uses 2D wavelet decompositions strategically interlayered with learnable convolutional layers to utilize the inductive prior of spatial sparsity of edges in visual data. Secondly, it uses a single memory tensor to capture inter-frame temporal information while avoiding the computational cost of previous memory-based schemes. Thirdly, it uses residual deformable convolutions for implicit inter-frame object alignment that improve upon deformable convolutions by enhancing spatial information in inter-frame feature differences. Architectural insights from our model can pave the way for real-time VSR on the edge, such as display devices for streaming data.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2502.01816
- https://arxiv.org/pdf/2502.01816
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4407184968
All OpenAlex metadata
Raw OpenAlex JSON
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https://openalex.org/W4407184968Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2502.01816Digital Object Identifier
- Title
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Low-Resource Video Super-Resolution using Memory, Wavelets, and Deformable ConvolutionsWork title
- Type
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preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
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2025Year of publication
- Publication date
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2025-02-03Full publication date if available
- Authors
-
Kavitha Viswanathan, Shashwat Pathak, Piyush Bharambe, Harsh Choudhary, Amit SethiList of authors in order
- Landing page
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https://arxiv.org/abs/2502.01816Publisher landing page
- PDF URL
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https://arxiv.org/pdf/2502.01816Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
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greenOpen access status per OpenAlex
- OA URL
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https://arxiv.org/pdf/2502.01816Direct OA link when available
- Concepts
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Residual, Computer science, Resolution (logic), Resource (disambiguation), Computer vision, Artificial intelligence, Algorithm, Computer networkTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
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10Other works algorithmically related by OpenAlex
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