Deep Laplacian Pyramid Network for Text Images Super-Resolution

11/26/2018
by   Hanh T. M. Tran, et al.
0

Convolutional neural networks have recently demonstrated interesting results for single image super-resolution. However, these networks were trained to deal with super-resolution problem on natural images. In this paper, we adapt a deep network, which was proposed for natural images superresolution, to single text image super-resolution. To evaluate the network, we present our database for single text image super-resolution. Moreover, we propose to combine Gradient Difference Loss (GDL) with L1/L2 loss to enhance edges in super-resolution image. Quantitative and qualitative evaluations on our dataset show that adding the GDL improves the super-resolution results.

READ FULL TEXT

Please sign up or login with your details

Forgot password? Click here to reset