Image Demorpher Using Machine Learning: Removing Fake Layers and Restoring Original Images - Harsha, Vasamsetti Sri; Sai, Tirumalasetti Yuvanth; Deepak, Gubbala Satya Sai; K., Amarendra; Areef, Shaik; Yellamma, Pachipala (International Journal of Intelligent Systems and Applications in Engineering, 2024)
This project introduces an innovative machine learning-based technique for the restoration of original images that have suffered distortion due to the presence of fake layers or other forms of image degradation. The approach entails the training of a deep neural network to acquire the ability to discern and reconstruct the mapping between morphed images and their corresponding pristine originals. To assess its effectiveness, a comprehensive dataset comprising images bearing diverse layers was meticulously curated, serving as the basis for both training and testing the deep neural network. The results demonstrate the remarkable proficiency of the proposed approach in accurately and faithfully restoring original images from their distorted counterparts. This advancement holds immense promise in the realm of machine learning-driven image restoration, with its potential applications spanning a multitude of fields. By providing a robust solution for image restoration from morphed images, this approach significantly enhances the precision and dependability of image-layered analyses, ultimately contributing to the advancement of numerous domains reliant on image fidelity.