Abstract:
Recently, the convolutional neural network with residual
learning models achieves high accuracy for single image
super-resolution with different scale factors. With adversarial
learning model, effective learning of transformation
function for the low-resolution input image to a highresolution
target image can be achieved. In this paper,
we propose a method for image and video super-resolution
using the recurrent generative adversarial network named
SR2GAN. In the proposed model (SR2GAN) we use recursive
learning for video super-resolution to overcome
the difficulty of learning transformation function for synthesizing
realistic high-resolution images. This recursive
approach helps to reduce the parameters with increasing
depth of the model. An extensive evaluation is performed
to examine the effectiveness of the proposed model, which
shows that SR2GAN performs better in terms of peak signal
to noise ratio (PSNR) and structural self-similarity index
(SSIM) as compared to the state-of-the-art methods for
super-resolution. For source code and supplementary material
visit: https://github.com/OmkarThawakar/SR2GAN/.
