Machine Learning Application for Evidence Image Enhancement
Sampangirama Reddy B. R., Ashendra Kumar Saxena, Binay Kumar Pandey, Sachin Gupta, Shashikala Gurpur, Sukhvinder Singh Dari, Dharmesh Dhabliya
Abstract
Taking into account the uses of ML in the field of vision, many practical vision systems' first processing stages include enhancing or reconstructing images. The goal of these tools is to enhance the quality of photos and give accurate data for making decisions based on appearance. In this research study, the authors examine three distinct types of neural networks: convolutional networks, residual networks, and generative countermeasure networks. There is a proposal for a model structure of a scalable supplementary generation network as part of a network that enhances evidence images as a generative countermeasure. The authors present the objective loss function definition, as well as the periodic consistency loss and the periodic perceptual consistency loss analysis. An in-depth solution framework for picture layering is offered once the problem's core aspects are explained. This approach implements multitasking with the help of adaptive feature learning, this provides a strong theoretical guarantee.