A long-distance imaging system can be strongly affected by atmospheric turbulence. In this paper, we propose a novel method for mitigating the effects of atmospheric distortion on observed images, particularly airborne turbulence which can severely degrade a region of interest (ROI). In order to extract accurate detail about objects behind the distorting layer, a simple and efficient frame selection method is proposed to select informative ROIs only from good quality frames. The ROIs in each frame are then registered to further reduce offsets and distortions. We solve the space-varying distortion problem using region-level fusion based on the dual tree complex wavelet transform. Finally, contrast enhancement is applied. We further propose a learning-based metric specifically for image quality assessment in the presence of atmospheric distortion. This is capable of estimating quality in both full and no-reference scenarios. The proposed method is shown to significantly outperform existing methods, providing enhanced situational awareness in a range of surveillance scenarios.

ABSTRACT:In this paper a novel turbulence mitigation algorithm is proposed based on Double density discrete wavelet transform based fusion technique. In this scheme DD-DWT is applied to ROI part of selected frame of video sequence to decompose image into sub-bands and then fusion technique is applied to each ROI frame so that obtained output video is distortion free.We compare our method with state-of-art criteria like DTCWT based fusion technique; we prove that our technique yields better results.

The world is facing several challenges due to the COVID-19 pandemic, which is causing severe social and economic disruption. The disruption has resulted in recession, unemployment, and social isolation and has caused an extreme burden on health care services. Artificial intelligence (AI) has a promising role in the healthcare sector by bringing many advantages to practicing clinicians, patients, and society. This article aims to determine the role of artificial intelligence in the ongoing COVID-19 pandemic. A quantitative methodology is used, and a literature review is done by using electronic databases such as PubMed, Google Scholar, and Scopus. The keywords used for this data research are "artificial intelligence" and "COVID 19"; "COVID 19" and "artificial intelligence". The results have shown that artificial intelligence has been extensively used in seven major domains during the current ongoing COVID-19 pandemic. These include screening and detection of COVID-19 transmission dynamics, diagnostics, disease monitoring and forecasting, disease outbreak containment, disease recovery and mortality, treatment and vaccination, and protection of healthcare workers. Artificial intelligence is a transformational force in the medical field. It assists in early detection of disease, real-time surveillance, diagnosis, treatment, disease containment, development of treatment and vaccinations, and reducing morbidity and mortality. Artificial intelligence will help us in the future to meet many challenges in a timely fashion through the prediction of pandemics, making stakeholders worldwide well prepared to deal with epidemics and pandemics in a systematic and organized manner, avoiding economic turmoil and unnecessary morbidity and mortality.