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Paper

Efficient INT8 Single-Image Super-Resolution via Deployment-Aware Quantization and Teacher-Guided Training

by Pham Phuong Nam Nguyen arxiv/2604.20291

Free2AITools Nexus Index

38.2

S: Semantic 50

Query-time baseline · scored live at search

A: Authority 0

P: Popularity 0

R: Recency 72

Q: Quality 60

Tech Context

Vital Performance —

Efficient single-image super-resolution (SISR) requires balancing reconstruction fidelity, model compactness, and robustness under low-bit deployment, which is especially challenging for x3 SR. We present a deployment-oriented quantized SISR framework based on an extract-refine-upsample design. The student performs most computation in the low-resolution space and uses a lightweight re-parameterizable backbone with PixelShuffle reconstruction, yielding a compact inference graph. To improve qua...

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Paper Information Summary
Entity Passport
Registry ID	2604.20291
License	arXiv
Provider	arxiv

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Cite this paper

Academic & Research Attribution

BibTeX

@misc{arxiv_2604_20291,
  author = {Pham Phuong Nam Nguyen},
  title = {Efficient INT8 Single-Image Super-Resolution via Deployment-Aware Quantization and Teacher-Guided Training Paper},
  year = {2026},
  howpublished = {\url{https://arxiv.org/abs/2604.20291}},
  note = {Accessed via Free2AITools.}
}

APA Style

Pham Phuong Nam Nguyen. (2026). Efficient INT8 Single-Image Super-Resolution via Deployment-Aware Quantization and Teacher-Guided Training [Paper]. Free2AITools. https://arxiv.org/abs/2604.20291

🔬Technical Deep Dive

Full Specifications [+]

⚖️ Free2AITools Nexus Index V2.0

Methodology How FNI works

Semantic (S) 50

Query-time baseline · scored live at search

Authority (A) 0

Popularity (P) 0

Recency (R) 72

Quality (Q) 60

💬 Index Insight

FNI V2.0 for Efficient INT8 Single-Image Super-Resolution via Deployment-Aware Quantization and Teacher-Guided Training: Authority (A:0), Popularity (P:0), Recency (R:72), Quality (Q:60). Semantic (S) is a query-time baseline scored live at search.

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HuggingFace API GitHub Metadata Arxiv Citation DB Methodology

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📝 Executive Summary

"Efficient single-image super-resolution (SISR) requires balancing reconstruction fidelity, model compactness, and robustness under low-bit deployment, which is especially challenging for x3 SR. We present a deployment-oriented quantized SISR framework based on an extract-refine-upsample design. The student performs most computation in the low-resolution space and uses a lightweight re-parameterizable backbone with PixelShuffle reconstruction, yielding a compact inference graph. To improve qua..."

❝ Cite Node

@article{Nguyen2026Efficient,
  title={Efficient INT8 Single-Image Super-Resolution via Deployment-Aware Quantization and Teacher-Guided Training},
  author={Pham Phuong Nam Nguyen},
  journal={arXiv preprint arXiv:2604.20291},
  year={2026}
}

👥 Collaborating Minds

Pham Phuong Nam Nguyen

🔗 Full Paper

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Read the full paper on arXiv

📊 Research Signals

📅1970Published

⏱️72RecencyFNI pillar

✅60QualityFNI pillar

🗂️cs.CVField

🏷️ Research Topics

image generation

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🆔 Identity & Source

id: 2604.20291
slug: 2604.20291
source: arxiv
author: Pham Phuong Nam Nguyen
license: arXiv
tags: arxiv:cs.CV

⚙️ Technical Specs

architecture: null
params billions: null
context length: null
pipeline tag

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stars: null
forks: null

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