ARXIV:2602.10744 · IMAGE QUALITY ASSESSMENT · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE

VerifiedSource: PDF linkedPartialPaperPack: 3 of 4 citation fields filledMissingMissing fields: authorsPartialProof: unverified proof status

Self-Supervised Image Super-Resolution Quality Assessment based on Content-Free Multi-Model Oriented Representation Learning

arXiv

Develop a no-reference image quality assessment tool using self-supervised learning for super-resolution applications in real-world scenarios.

Blocked on Code›Score6.0Evidence unverified

Opportunity summary

Pain Develop a no-reference image quality assessment tool using self-supervised learning for super-resolution applications in real-world scenarios.

Evidence 0 refs | 0 sources | 17% coverage

Blocker Evidence unverified

Open Build Read PDF Signal Canvas Track

PROBLEM

Develop a no-reference image quality assessment tool using self-supervised learning for super-resolution applications in real-world scenarios. In contrast to SR artifacts arising from synthetic LR images created under well-defined scenarios, those distortions are highly…

METHOD

Full abstract

Super-resolution (SR) applied to real-world low-resolution (LR) images often results in complex, irregular degradations that stem from the inherent complexity of natural scene acquisition. In contrast to SR artifacts arising from synthetic LR images created under well-defined scenarios, those distortions are highly unpredictable and vary significantly across different real-life contexts. Consequently, assessing the quality of SR images (SR-IQA) obtained from realistic LR, remains a challenging and underexplored problem. In this work, we introduce a no-reference SR-IQA approach tailored for such highly ill-posed realistic settings. The proposed method enables domain-adaptive IQA for real-world SR applications, particularly in data-scarce domains. We hypothesize that degradations in super-resolved images are strongly dependent on the underlying SR algorithms, rather than being solely determined by image content. To this end, we introduce a self-supervised learning (SSL) strategy that first pretrains multiple SR model oriented representations in a pretext stage. Our contrastive learning framework forms positive pairs from images produced by the same SR model and negative pairs from those generated by different methods, independent of image content. The proposed approach S3 RIQA, further incorporates targeted preprocessing to extract complementary quality information and an auxiliary task to better handle the various degradation profiles associated with different SR scaling factors. To this end, we constructed a new dataset, SRMORSS, to support unsupervised pretext training; it includes a wide range of SR algorithms applied to numerous real LR images, which addresses a gap in existing datasets. Experiments on real SR-IQA benchmarks demonstrate that S3 RIQA consistently outperforms most state-of-the-art relevant metrics.

RESULT

ScienceToStartup currently rates this 6.0/10 on the public viability pass. Super-resolution (SR) applied to real-world low-resolution (LR) images often results in complex, irregular degradations that stem from the inherent complexity of natural scene acquisition.

WHY NOW

Image Quality Assessment moved forward this cycle; last verified April 2026. Public score 6.0/10.

Continue into Read for claims, analysis, references, and neighboring papers.

Opportunity summary

Score6.0

PainDevelop a no-reference image quality assessment tool using self-supervised learning for super-resolution applications in real-world scenarios.

Evidence0 refs | 0 sources | 17% coverage

Blockermissing authors

Analysis summary

Develop a no-reference image quality assessment tool using self-supervised learning for super-resolution applications in real-world scenarios.

VerifiedSource: PDF linkedPartialPaperPack: 3 of 4 citation fields filledMissingMissing fields: authorsPartialProof: unverified proof status

ARXIV:2602.10744 · IMAGE QUALITY ASSESSMENT · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE

VerifiedSource: PDF linkedPartialPaperPack: 3 of 4 citation fields filledMissingMissing fields: authorsPartialProof: unverified proof status

Self-Supervised Image Super-Resolution Quality Assessment based on Content-Free Multi-Model Oriented Representation Learning

arXiv

Develop a no-reference image quality assessment tool using self-supervised learning for super-resolution applications in real-world scenarios.

Blocked on Code›Score6.0Evidence unverified

Opportunity summary

Pain Develop a no-reference image quality assessment tool using self-supervised learning for super-resolution applications in real-world scenarios.

Evidence 0 refs | 0 sources | 17% coverage

Blocker Evidence unverified

Open Build Read PDF Signal Canvas Track

PROBLEM

METHOD

Full abstract

RESULT

WHY NOW

Image Quality Assessment moved forward this cycle; last verified April 2026. Public score 6.0/10.

Continue into Read for claims, analysis, references, and neighboring papers.

Opportunity summary

Score6.0

PainDevelop a no-reference image quality assessment tool using self-supervised learning for super-resolution applications in real-world scenarios.

Evidence0 refs | 0 sources | 17% coverage

Blockermissing authors

Analysis summary

Develop a no-reference image quality assessment tool using self-supervised learning for super-resolution applications in real-world scenarios.

VerifiedSource: PDF linkedPartialPaperPack: 3 of 4 citation fields filledMissingMissing fields: authorsPartialProof: unverified proof status

Paper Pack

10.48550/arXiv.2602.10744

Self-Supervised Image Super-Resolution Quality Assessment based on Content-Free Multi-Model Oriented Representation Learning

Develop a no-reference image quality assessment tool using self-supervised learning for super-resolution applications in real-world scenarios.

Abstract

Source availability

PDF linked

The paper record includes a public PDF URL.

Extraction status

Derived fallback

Read summaries are estimated from adjacent metadata, not verified extraction rows.

Proof status

unverified

0 refs; 0 sources; 17% coverage.

What was readable

linkedon filenot materializedderived fallback79 indexednot indexed

Derived fallback: Estimated from adjacent evidence; not verified from source.

Viability

6.0

Time to MVP

MVP estimate missing

Commercial

No commercial flags on file

Export

Preparing verified analysis

lens / founder

PROBLEM

METHOD

RESULT

WHY NOW

Image Quality Assessment moved forward this cycle; last verified April 2026. Public score 6.0/10.

Claim map

Abstract-backed public claims while anchored extraction refreshes.

Strong 0Mixed 0Weak 4

Evidencepartial
Develop a no-reference image quality assessment tool using self-supervised learning for super-resolution applications in real-world scenarios. In contrast to SR artifacts arising from synthetic LR images created under well-defined scenarios, those distortions are highly unpredictable and vary significantly across different real-life contexts.
Implicationpartial
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
Verificationpartial
partial
Evidencepartial
Super-resolution (SR) applied to real-world low-resolution (LR) images often results in complex, irregular degradations that stem from the inherent complexity of natural scene acquisition. In contrast to SR artifacts arising from synthetic LR images created under well-defined scenarios, those distortions are highly unpredictable and vary significantly across different real-life contexts.
Implicationpartial
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
Verificationpartial
partial
Evidencepartial
ScienceToStartup currently rates this 6.0/10 on the public viability pass. Super-resolution (SR) applied to real-world low-resolution (LR) images often results in complex, irregular degradations that stem from the inherent complexity of natural scene acquisition.
Implicationpartial
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
Verificationpartial
partial
Evidencepartial
Image Quality Assessment moved forward this cycle; last verified April 2026. Public score 6.0/10.
Implicationpartial
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
Verificationpartial
partial

Constellation map

Paper-native neighborhood for concepts, methods, materials, markets, and competitors. Missing lanes stay labeled instead of disappearing behind commercialization gates.

Open full Signal Canvas

Concepts

not indexed

Methods

Materials

PDF linked

Markets

Image Quality Assessment

Competitors

not indexed

Competitive landscape

Develop a no-reference image quality assessment tool using self-supervised learning for super-resolution applications in real-world scenarios.

Segment

Image Quality Assessment

Adoption evidence

No public code link in the paper record yet

Commercial read

6.0/10 public viability

Direct

not classified

Adjacent

not classified

Substitute

not classified

Unknown

not classified

Buzz

No indexed public discussion is attached to 2602.10744 yet. That is a visibility signal, not a blank module: the monitor is watching the public channels below.

Hacker News

Not indexed yet

Bluesky

Not indexed yet

PDF

Preview the source document here, or use the hero PDF action for a new tab.

References(79)

Deep learning-based remote sensing image super-resolution: Recent advances and challenges

2026Jiawei Yang, Hongliang Ren et al.

Frequency and spatial-channel cross transformer for lightweight astronomical image super-resolution

2025Jiawei Miao, Liangping Tu et al.

A comprehensive review on deep supervision in computer vision

2025Renjie Li, Xinyi Wang et al.

Biology-driven assessment of deep learning super-resolution imaging of the porosity network in dentin

2025Lauren Anderson, L. Chatelain et al.

VQualA 2025 Challenge on Image Super-Resolution Generated Content Quality Assessment: Methods and Results

2025Yixiao Li, Xin Li et al.

Real-world super-resolution with VLM-based degradation prior learning

2025Xiaxu Chen, Duixu Mao et al.

TRIQA: Image Quality Assessment by Contrastive Pretraining on Ordered Distortion Triplets

2025Rajesh Sureddi, Saman Zadtootaghaj et al.

Distilling Spatially-Heterogeneous Distortion Perception for Blind Image Quality Assessment

2025Xudong Li, Wenjie Nie et al.

Deep learning-based image super resolution methods in microscopy – a review

2025A. Jansche, P. Krawczyk et al.

Scene Perceived Image Perceptual Score (SPIPS): Combining Global and Local Perception for Image Quality Assessment

2025Zhiqiang Lao, Hong Heather Yu

Toward Generalized Image Quality Assessment: Relaxing the Perfect Reference Quality Assumption

2025Du Chen, Tianhe Wu et al.

CATANet: Efficient Content-Aware Token Aggregation for Lightweight Image Super-Resolution

2025Xin Liu, Jie Liu et al.

A Self-Attention Guided Approach for Advanced Underwater Image Super-Resolution With Depth Awareness

2025Muhammad Fahad Nasir, M. Rehman et al.

Arbitrary-steps Image Super-resolution via Diffusion Inversion

2024Zongsheng Yue, Kang Liao et al.

HIIF: Hierarchical Encoding based Implicit Image Function for Continuous Super-resolution

2024Yuxuan Jiang, Ho Man Kwan et al.

A survey of super-resolution image quality assessment

2024Lei Shu, Qinru Zhu et al.

Subjective and Objective Quality Evaluation of Super-Resolution Enhanced Broadcast Images on a Novel SR-IQA Dataset

2024Yongrok Kim, Junha Shin et al.

Few-Shot Image Quality Assessment via Adaptation of Vision-Language Models

2024Xudong Li, Zihao Huang et al.

Perception-Driven Similarity-Clarity Tradeoff for Image Super-Resolution Quality Assessment

2024Keke Zhang, Tiesong Zhao et al.

Blind Image Quality Assessment Based on Geometric Order Learning

2024Nyeong-Ho Shin, Seon-Ho Lee et al.

Showing 20 of 79 references

CITED BY

No citing papers are indexed in the public S2S graph yet. This is an explicit zero-signal state, not a hidden lookup.

Foundation

Prior WorkRAW-Domain Degradation Models for Realistic Smartphone Super-Resolution

6.0

Extension

none indexed

Commercially relevant

Higher ViabilityRefReward-SR: LR-Conditioned Reward Modeling for Preference-Aligned Super-Resolution

7.0

Higher ViabilityInstanceRSR: Real-World Super-Resolution via Instance-Aware Representation Alignment

7.0

Higher ViabilityQUSR: Quality-Aware and Uncertainty-Guided Image Super-Resolution Diffusion Model

8.0

Higher ViabilityBeyond Visual Fidelity: Benchmarking Super-Resolution Models for Large-Scale Remote Sensing Imagery via Downstream Task Integration

7.0

Higher ViabilityPR-IQA: Partial-Reference Image Quality Assessment for Diffusion-Based Novel View Synthesis

7.0

Higher ViabilityNTIRE 2026 The 3rd Restore Any Image Model (RAIM) Challenge: Professional Image Quality Assessment (Track 1)

7.0

Higher ViabilityQualiTeacher: Quality-Conditioned Pseudo-Labeling for Real-World Image Restoration

7.0

Higher ViabilityOARS: Process-Aware Online Alignment for Generative Real-World Image Super-Resolution

7.0

Conflicting

Competing ApproachCausal Disentanglement for Full-Reference Image Quality Assessment

4.0

Related Resources

What is APEX in image quality assessment?(question)
What is APEX in image quality assessment?(question)
What is APEX in image quality assessment?(question)

Owned Distribution

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Agent drawer

5 surfaces preserved for agents. Humans can ignore.

Developer contracts, payload previews, evidence maps, and run controls stay here instead of the Read, Build, and Track workspace.

Run context

Paper: 2602.10744
Route: /paper/self-supervised-image-super-resolution-quality-assessment-based-on-content-free-multi-model-oriented-representation-lear
Active tab: read
Artifact: self-supervised-image-super-resolution-quality-assessment-based-on-content-free-multi-model-oriented-representation-lear

Available agents

Read extractor
Build planner
Track monitor
Competitive mapper
Related-paper scout

API/MCP endpoints

REST paper pack API/api/v1/paper/self-supervised-image-super-resolution-quality-assessment-based-on-content-free-multi-model-oriented-representation-lear/paper-pack
REST build passport API/api/v1/paper/self-supervised-image-super-resolution-quality-assessment-based-on-content-free-multi-model-oriented-representation-lear/build-passport
REST OpenAPI/api/openapi.json
MCP descriptor/api/mcp
MCP resourcesciencetostartup://surfaces/paper-workspace

Tool contracts

paper_packbuild_passportopportunity_kernelforesightsource_proofevidence_state

Payload preview

Inspect payload

{
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  "paper_id": "fc60ee88-0aa8-4000-b063-9de6a12b29e9",
  "arxiv_id": "2602.10744",
  "canonical_route": "/paper/self-supervised-image-super-resolution-quality-assessment-based-on-content-free-multi-model-oriented-representation-lear",
  "active_tab": "synced from current hash by the drawer client",
  "selected_artifact": "self-supervised-image-super-resolution-quality-assessment-based-on-content-free-multi-model-oriented-representation-lear",
  "endpoints": {
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    "build_passport": "/api/v1/paper/self-supervised-image-super-resolution-quality-assessment-based-on-content-free-multi-model-oriented-representation-lear/build-passport",
    "mcp_resource": "sciencetostartup://surfaces/paper-workspace"
  }
}

Schema validation

paper-r2 contract: present
JSON-LD twin: SSR emitted
OpenAPI path parity: /api/openapi.json
MCP resource parity: paper-workspace

Job trace

queued: drawer opened by user action
running: inspect or copy payload
succeeded: payload available in SSR
failed: route errors appear in evidence cards

Evidence map

sources used: page freshness, source proof anchors, JSON-LD
missing sources: exposed by PaperPack and EvidenceState chips
derived fallbacks: marked unverified before handoff

Page Freshness

Canonical route, proof status, last verified, refs, sources, and coverage.

Page Freshness

Paper proof surface

Canonical route: /paper/self-supervised-image-super-resolution-quality-assessment-based-on-content-free-multi-model-oriented-representation-lear

stale

Proof freshness: stale
Proof status: unverified
Display score: 6/10
Last proof check: 2026-04-02
Score updated: 2026-04-02
Score fresh until: 2026-05-02
References: 0
Source count: 0
Coverage: 17%

This page is showing the last landed evidence receipt and score bundle because the latest proof data is outside the freshness window.

OpenAlex: pending — this preprint is not yet indexed by OpenAlex.

Agent Handoff

Endpoint list, payload shape, route context, and copyable handoff data.

Agent Handoff

Self-Supervised Image Super-Resolution Quality Assessment based on Content-Free Multi-Model Oriented Representation Learning

Canonical ID self-supervised-image-super-resolution-quality-assessment-based-on-content-free-multi-model-oriented-representation-lear | Route /paper/self-supervised-image-super-resolution-quality-assessment-based-on-content-free-multi-model-oriented-representation-lear

REST example

curl https://sciencetostartup.com/api/v1/agent-handoff/paper/self-supervised-image-super-resolution-quality-assessment-based-on-content-free-multi-model-oriented-representation-lear

MCP example

{
  "tool": "get_paper",
  "arguments": {
    "arxiv_id": "2602.10744"
  }
}

source_context

{
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  "mode": "paper",
  "query": "Self-Supervised Image Super-Resolution Quality Assessment based on Content-Free Multi-Model Oriented Representation Learning",
  "normalized_query": "2602.10744",
  "route": "/paper/self-supervised-image-super-resolution-quality-assessment-based-on-content-free-multi-model-oriented-representation-lear",
  "paper_ref": "self-supervised-image-super-resolution-quality-assessment-based-on-content-free-multi-model-oriented-representation-lear",
  "topic_slug": null,
  "benchmark_ref": null,
  "dataset_ref": null
}

Buildability Receipt

Verdict, compute envelope, blockers, signature state, and receipt links.

Paper proof page receipt window

Watch and verify: Self-Supervised Image Super-Resolution Quality Assessment based on Content-Free Multi-Model Oriented Representation Learning

/buildability/self-supervised-image-super-resolution-quality-assessment-based-on-content-free-multi-model-oriented-representation-lear

Watchwatch

Subject: Self-Supervised Image Super-Resolution Quality Assessment based on Content-Free Multi-Model Oriented Representation Learning

Verdict

Watch

Verdict is Watch because viability or proof quality is intermediate and should be re-evaluated before execution.

Time to first demo

Insufficient data

No first-demo timestamp, owner estimate, or elapsed demo receipt is attached to this surface.

Compute envelope

Structured compute envelope

Insufficient data

No data, compute, hardware, memory, latency, dependency, or serving requirement receipt is attached.

Evidence ids

Receipt path

/buildability/self-supervised-image-super-resolution-quality-assessment-based-on-content-free-multi-model-oriented-representation-lear

Paper ref

self-supervised-image-super-resolution-quality-assessment-based-on-content-free-multi-model-oriented-representation-lear

arXiv id

2602.10744

Freshness

Generated at

2026-04-02T02:30:40.136Z

Evidence freshness

stale

Last verification

2026-04-02T02:30:40.136Z

Sources

References

Coverage

17%

Hash state

Lineage hash

23e7fa62edfa24fe4bc0b363039ddfc598b1871492eb6f75b9aa4458de677e7d

Canonical opportunity-kernel lineage hash.

Signature state

External signature

unsigned_external

No founder, registry, pilot, or production-adoption signature is attached to this receipt.

Verification

not_verified

Verification is blocked until an external signature is provided.

Blockers

Missing: repo_url
Missing: references
Missing: proof_status
Missing: distribution_readiness_scores
Missing: paper_extraction_scorecards
Unknown: distribution readiness has not been computed yet
Unknown: proof verification has not been recorded yet

Verification pending / evidence receipt incomplete

repo_url

references

Missing proof, requirement, signature, approval, adoption, or telemetry fields are blockers and must not be inferred.

Open receipt API receipt Build Loop Signal Canvas Proof divergence Divergence API Brier outcomes API

Source Proof anchors

Visual citations from the paper document graph.

JSON-LD twin

The application/ld+json payload rendered for agents.

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Self-Supervised Image Super-Resolution Quality Assessment based on Content-Free Multi-Model Oriented Representation Learning

Self-Supervised Image Super-Resolution Quality Assessment based on Content-Free Multi-Model Oriented Representation Learning

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Claim map

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References(79)

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Subscribe to the weekly brief

Build artifacts

Brief

Experiment plan

Validation checklist

Scientific founder

Translational engineer

Domain operator

GTM lead

Regulatory/clinical advisor

Timeline