EXPLORING DEEP LEARNING METHODS FOR LOW NUMERICAL APERTURE TO HIGH NUMERICAL APERTURE RESOLUTION ENHANCEMENT IN CONFOCAL MICROSCOPY

Confocal microscopy is a widely used tool that provides valuable morphological and functional information within cells and tissues. A major advantage of confocal microscopy is its ability to record multi-color and optically sectioned images. A major drawback to confocal microscopy is its diffraction-limited spatial resolution. Though techniques have been developed that break this limit in confocal microscopy, they require additional hardware or accurate estimates of the system’s impulse response (e.g., point spread function). Here we investigate two deep learning-based models, the cGAN and cycleGAN, trained with low-resolution (LR) and high-resolution (HR) confocal images to improve spatial resolution in confocal microscopy. Our findings conclude that the cGAN can accurately produce HR images if the training set contains images with a high signal-to-noise ratio. We have also found that the cycleGAN model has the potential to perform as the cGAN model but without the requirement of using paired inputs

Cloud Computing Service Selection Algorithm

In modern world Cloud Computing is one of the most promising and evolving areas of computer science. As time passes by more and more cloud devices are being setup. Similarly more companies and industries are opting for cloud services, etc. Cloud has made up a virtual reality of the practical world. It oers online storage space, online infrastructure, online platforms, etc to make our everyday computing experience easier and cheaper. One of the aspects of cloud computing is provision of servers to execute our programs which comes under Infrastructure as a Service (IaaS). In this project we have focused on devising an algorithm to schedule jobs and allocate servers in cloud systems. The algorithm is ecient as it provides optimal allocation. It maximizes the number of job requests that can be processed in unit time while conserving energy and keeping the costs low. The said optimal allocation is achieved by reducing the idle time of nodes of active servers and reducing the total number of servers used. We implemented our algorithm using random data sets of job requests with dierent attributes and generated simulations in forms of graphs. The graphs prove the eciency of job scheduling algorithm and the server allocation for which we used Best Fit algorithm of the Bin Packing problem. Finally a detailed analysis is given and future works are stated