Devnagari Handwritten Characters Image Super-Resolution Based On SRGAN

Abstract

The difficulty in machine learning based image super-resolution is to generate high-frequency component in an image without introducing any artifacts. In this paper, Devnagari handwritten characters image using a generative adversarial network with a classifier is generated in high-resolution which is also classifiable. The generator architecture is modified by removing all the batch normalization layes in generator architecture with a residual in residual dense block. Batch normalization is removed because it produces unwanted artifacts in the generated images. A Devnagari handwritten characters classifier is built using CNN. The classifier is used in the network to calculate the content loss. The adversarial loss is obtained from the GAN architecture and both of the losses are added to obtain total loss. Generated HR images is validated using six different evaluation metrics among which MSE, PSNR determines pixel-wise difference and SSIM compares images perceptually. Similarly FID is used to measure the statistical similarity between the batch of generated images and its original batch. Finally the Gradient similarity is used to assess the quality of the generated image. From the experimental results we obtain MSE, PSNR and SSIM as 0.0507, 12.95(dB) and 0.8172 respectively. Similarly, the FID value obtained was 27.5 with the classification accuracy of image data of 98%. The gradient similarity between the generated image and the ground truth obtained was 0.9124.