Swarm-Optimized Turbulence Effects in RGB Image Transmission Over DP/FSO System
Bibliographic record
Abstract
Free-space optical (FSO) communication systems face significant challenges in maintaining image fidelity under atmospheric turbulence, particularly for high-bandwidth RGB applications. This paper introduces a novel dual-polarized (DP) FSO transmission framework enhanced by particle swarm optimization (PSO) to achieve turbulence-resilient RGB image recovery. By exploiting polarization-division multiplexing, the system doubles transmission capacity while employing a physics-informed PSO algorithm to dynamically optimize a multi-stage correction pipeline comprising Wiener deconvolution, gamma correction, histogram matching, and non-local denoising. The proposed method uniquely correlates optimized restoration parameters with propagation distance, enabling simultaneous image recovery and ranging under Gamma-Gamma turbulence modeling. Experimental validation across weak and strong turbulence conditions $\left( {C_n^2 = {{10}^{ - 17}} - {{10}^{ - 13}}{{\text{m}}^{ - 2/3}}} \right)$ demonstrates significant improvements in structure-similarity-index-measure (SSIM), particularly achieving 0.4397 SSIM recovery for Llama images at 2.9 km under strong turbulence and a 119% improvement over uncorrected results. Comparative analysis shows superior performance to deep learning baselines, with 11.2% higher SSIM at 2 km distances. By combining turbulence mitigation with accurate ranging, this framework advances high-fidelity FSO imaging for remote sensing and surveillance applications.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".