Image Completion using Deep Convolutional Generative Adversarial Networks

Abstract

Deep learning recently became the state-of-the-art in many pattern recognition tasks. Advance-ment of computational power and big datasets brings opportunity to use deep learning methods for image processing. We have used deep convolutional generative adversarial networks (DCGAN) to do various image processing tasks such as deconvolution , denoising and super-resolution. With DCGAN we can use a single architecture to perform different image processing tasks . While the results sometimes shows slightly lower PSNR for DCGAN compared to traditional methods but it tries to achieve competitive psnr scores. Thus , it allows to view quite appealing then other methods While it can learn from big data-sets very efficiently and allows itself to add high-frequency details automatically which traditional methods can’t. The architectgure in DCGAN is based on two neural networks of generator and discriminator which both tries to deceive each other and allows it to generate more appealing and realistic images from the datasets.