Visible property enhancement techniques of IoT cameras using machine learning techniques

Abstract

Perceiving a sight in low light is challenging due to low SNR and photon counts. Deeper learning is a kind of machine learning that is revolutionising picture identification and computer perception. In this study, deeper learning will be used to enhance low-light picture filtering. To do this, a literature review will be performed to gather inspiration for methods and features that may be applied to the final networks. A fully functioning deeper learning picture filtering system will then be created, allowing networks to be trained using guided learning and the filtered resulting images to be recorded to files. With its output pictures plainly showing it was filtering low-light shots, the network functioned effectively. To maximise the network's potential, it must be run for a longer length of time.