<![CDATA[This systematic literature study seeks to computationally develop the optimum picture restoration architecture to handle the dual analog-digital degradation (strip noise and motion blur) of Slow Scan Television (SSTV) transmissions in narrowband radio frequencies during emergency operations. The study critically assessed 28 peer-reviewed algorithmic frameworks published between 2007 and 2025 using PRISMA criteria. The data synthesis procedure used a comparative analysis matrix to evaluate algorithms' quantitative efficacy, including PSNR and SSIM, against specific analogue failure instances. Under compounding degradation, individual computational methods fail, as shown by the synthesis. When exposed to severe analogue noise, unguided generative models hallucinate, whereas conventional spatial filters degrade edges. Comparative empirical research shows that a cascaded hybrid framework works best. Using a 5x5 median filter with 1D directed filtering as pre-processing suppresses high-density impulsive anomalies, improving baseline PSNR by 2.4 dB. The kernel-guided diffusion model over the pre-cleaned matrix accurately reconstructs structural weaknesses, raising SSIM indices to 0.92 even in datasets with significant oscillator blur. A quantitatively validated, domain-specific restoration process that combines spatial denoising with advanced generative priors is the main contribution of this research. This study scientifically proves that kernel-based diffusion models need spatial variance pre-filtering to work in radio-degraded scenarios, providing a reliable emergency visual communication framework for authentic signal reconstruction in internet-deprived environments.]]>
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