Reconstruction Old Students Image Using The Autoencoder Method

Abstract

Image Processing is image processing with a digital computer to produce new images according to the user's wishes. One implementation is to reconstruct the image. Through the extraction stages can get the characteristics of an image. The algorithm used is Adam Optimization, an extension of the stochastic gradient reduction that has seen wider adoption for deep learning applications in computer vision and natural language processing. In this study, we use the autoencoder technique, one variant of artificial neural networks generally used to "encode" data. The autoencoder is trained to produce the same output as the input. This image reconstruction aims to process an image whose quality is not very clear, to be precise. This, if possible, can be used to detect someone's face from photos. In reconstructing this image through the encode and decode process by defining Conv2D and Maxpool, it is processed into training with epoch 100 times while for the prediction process using Keras library. Then, the last one gets an accuracy of 0,022. The result is the output of the reconstructed image and calculation graph