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 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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 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".