A novel effectiveness evaluation method of smokescreen jamming based on the fusion of directional gradients and local texture features

Smokescreen jamming is one of the most common types interference in optical imaging. However, there are few tools available to assess effectiveness imaging presence smoke screen interference, and them qualitative nature. There an urgent need for targeted quantitative assessment methods. Therefore, a novel evaluation method (EEMSSJ) based on histogram oriented gradient local binary pattern feature proposed. According influence tracking effect, proposed weights fuses directional feature, texture cosine similarity, luminance image finally obtains EEMSSJ index. The index provides from perspective, which can effectively improve accuracy analysis judgment smokescreen situation provide strong reference basis making timely adjustments deployment. validated by experiments. Experimental results show that achieve high level agreement with subjective experimental data possess sensitivity robustness. more accurate representation dynamic disturbance than correlation multi-scale structural similarity method.