A Mamba-Based Approach for Super-Resolution and Speckle Noise Reconstruction of SAR Images in Remote Sensing Applications
Bibliographic record
Abstract
Synthetic aperture radar (SAR) imagery is often degraded by multiplicative speckle and limited resolution, hindering reliable interpretation. To address this, we proposed MambaR-ESRGAN, a unified restoration framework that jointly performs super-resolution and speckle noise denoising by embedding Mamba's state-space modeling into a Real-ESRGAN backbone. Central to the design are Residual-in-Residual Mamba (RRDM) blocks, which efficiently capture long-range context while preserving fine local structure, enabling accurate suppression of speckle without losing details on edges. MambaR-ESRGAN is computationally efficient and delivers consistent quality gains over strong baselines. On representative benchmarks, it improves PSNR by 9.4% (from 24.16 dB to 26.43 dB) and SSIM by 16.4% (from 0.7065 to 0.8226) compared with the original Real-ESRGAN, with qualitative results showing sharper edges and better preservation of linear features. Extensive experiments across diverse SAR datasets confirm robustness and generalizability, indicating the method's suitability for downstream applications such as flood delineation, road mapping, and ship detection.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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 teacher head, 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".