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Record W4414221862 · doi:10.1109/jstars.2025.3608126

RADiffSR: A Diffusion Model for Remote Sensing Image Super-Resolution Fusing Residual Attention and Cross-Scale Dynamic Gating

2025· article· en· W4414221862 on OpenAlexaboutno aff
Jiajun Chang, Jiguang Dai, Tengda Zhang

Bibliographic record

VenueIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · 2025
Typearticle
Languageen
FieldEngineering
TopicAdvanced Image Fusion Techniques
Canadian institutionsnot available
FundersNational Natural Science Foundation of China
KeywordsFeature (linguistics)Multispectral imageBlock (permutation group theory)Kernel (algebra)Feature extractionPattern recognition (psychology)Channel (broadcasting)FootprintLand coverDistortion (music)

Abstract

fetched live from OpenAlex

Remote sensing image super-resolution (SR) technology is critical for enhancing the fine interpretation capability of large-scale land cover elements. However, existing methods are constrained by three core deficiencies: insufficient information interaction in multispectral channel modeling, lack of spatiotemporal continuity modeling for geographic entities, and failure of cross-scale feature geometric alignment. These deficiencies lead to coupled challenges in reconstructed images, including morphological discontinuities of extensive geographic features and texture artifact proliferation. This paper proposes a remote sensing image SR algorithm based on the diffusion probabilistic model (DPM), referred to as RADiffSR. First, a Residual-Attention Enhancement Block (RAE Block) is designed. It integrates residuals and Nonlinear Activation-Free Block to form a dual-domain attention mechanism, which synchronously optimizes feature response weights in the spatial and spectral domains, alleviating the deficiency in correlation representation between multispectral channels. Second, we introduce large-kernel convolutional layers to construct multi-level receptive field architectures aligned with geographic entity scale characteristics, modeling extensive terrain continuity through enlarged kernel sizes while incorporating inverted bottleneck ConvFFN structures to deepen feature extraction and implicitly enhance high-frequency texture retention. Finally, a feature manifold alignment strategy is implemented with dynamic gating mechanisms between encoder-decoder pathways to regulate cross-scale feature propagation weights, suppressing semantic distortion and high-frequency information loss. We construct the GF7-SR super-resolution dataset based on GF-7 satellite imagery, encompassing diverse typical land cover scenarios including mountainous houses, farmland, forests, and water bodies for model training and testing. Experiments demonstrate that RADiffSR achieves 36.39 dB and 28.36 dB PSNR on GF7-SR and Toronto datasets respectively, significantly outperforming state-of-the-art methods.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.954
Threshold uncertainty score0.878

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.013
GPT teacher head0.263
Teacher spread0.250 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreMethods

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations2
Published2025
Admission routes1
Has abstractyes

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