Study of Image In painting Using Generative Adversarial Network Architecture
Image In painting is the process of reconstructing lost or deteriorated parts of images and videos. It is an important problem in computer vision and holds several importance in many imaging and graphics applications, e.g. restoring old photos and videos, automatic scene editing, denoising, compression and image based rendering. The traditional method of Image In painting which are mostly based on machine learning models work well for background in painting, they cannot hallucinate novel image contents for challenging tasks such as in painting of faces and complex objects as well as failing to capture high level objects semantics. It has been discovered that by simply introducing a small bit of noise to the original data, most mainstream neural nets may be readily misled into misclassifying items. This is because most machine learning models only learn from a little quantity of data and the input-to-output mapping is nearly linear, which is a major disadvantage and leads to overfitting. The present method where we use GANs, or Generative Adversarial Networks, are a type of generative modelling that employs deep learning techniques such as convolutional neural networks. GANs has a capability of learning from data that is unstructured or unlabeled, the algorithms try to learn using method of feature extraction which is very different, more reliable and fully automatic. Celeb Faces Attributes Dataset (Celeb A) is large scale face attributes dataset with more than 200K celebrity images, each with 40 attributes annotations.