ADAPTIVE ALGORITHM FOR RESTORATION OF LOSSY COMPRESSED IMAGES

This work deals with the restoration of lossy compressed image by the use of a metaheuristic which is the Particle Swarm Optimization Algorithm. This algorithm was designed and adopted by the introduction of the Search Efficiency Function for the blind restoration of blurred images and has given excellent results. So, in the present paper we try to apply it in the enhancement of lossy decompressed images, and this application constitutes the contribution of this work. Images used have been compressed by two different compression methods, fractal and JPEG, and with different compression rates. The experimental results obtained were excellent

Artificial Intelligence Frameworks to Detect and Investigate the Pathophysiology of Spaceflight Associated Neuro-Ocular Syndrome (SANS)

Spaceflight associated neuro-ocular syndrome (SANS) is a unique phenomenon that has been observed in astronauts who have undergone long-duration spaceflight (LDSF). The syndrome is characterized by distinct imaging and clinical findings including optic disc edema, hyperopic refractive shift, posterior globe flattening, and choroidal folds. SANS serves a large barrier to planetary spaceflight such as a mission to Mars and has been noted by the National Aeronautics and Space Administration (NASA) as a high risk based on its likelihood to occur and its severity to human health and mission performance. While it is a large barrier to future spaceflight, the underlying etiology of SANS is not well understood. Current ophthalmic imaging onboard the International Space Station (ISS) has provided further insights into SANS. However, the spaceflight environment presents with unique challenges and limitations to further understand this microgravity-induced phenomenon. The advent of artificial intelligence (AI) has revolutionized the field of imaging in ophthalmology, particularly in detection and monitoring. In this manuscript, we describe the current hypothesized pathophysiology of SANS and the medical diagnostic limitations during spaceflight to further understand its pathogenesis. We then introduce and describe various AI frameworks that can be applied to ophthalmic imaging onboard the ISS to further understand SANS including supervised/unsupervised learning, generative adversarial networks, and transfer learning. We conclude by describing current research in this area to further understand SANS with the goal of enabling deeper insights into SANS and safer spaceflight for future missions