StereoMamba+: A Novel Stereo Image Super-Resolution Framework With Adaptive Dependency Capture and Enhanced Feature Fusion
Bibliographic record
Abstract
Stereo image Super-Resolution (SR) aims to enhance image resolution by leveraging complementary information in stereo pairs. Convolutional Neural Networks (CNNs), widely used in stereo image SR for their strong local pattern extraction capabilities, often fail to capture long-range dependencies critical for stereo correspondence. On the other hand, Swin Transformers have demonstrated superior performance in modeling long-range dependencies for stereo image SR tasks. However, their computational complexity scales quadratically with the window size, leading to a trade-off between global receptive fields and computational efficiency. To tackle these challenges, we propose StereoMamba+, a novel stereo image SR method designed to adaptively capture both local and global dependencies in stereo pairs. Leveraging the Mamba architecture as its backbone, StereoMamba+ integrates an Adaptive State Space Module (ASSM) that efficiently extracts and fuses global and local features, maintaining linear computational complexity. Additionally, a Gated Enhanced Feed-Forward Network (GEFN) selectively amplifies essential features and depth cues, and a Residual Frequency Block (RFB) is employed to capture global features in the frequency domain. To further enhance stereo correspondence, we introduce a Stereo Bi-Directional Cross Attention Module (SBCAM), aligning unique features along both horizontal and vertical epipolar lines to improve stereo consistency. Extensive experiments demonstrate that our proposed StereoMamba+ method achieves state-of-the-art performance on 2× and 4× stereo image SR tasks, delivering PSNR improvements of up to 0.45dB, while maintaining competitive parameter efficiency compared to existing methods.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".