EnhanceNet

This is an implementation of the paper "EnhanceNet: Single Image Super-Resolution Through Automated Texture Synthesis" by M. Sajjadi et al., (2017).

Model Architecture

Fully convolutional network architecture for 4x super-resolution which only learns the residual between the bicubic interpolation of the input and the ground truth. USes 3×3 convolution kernels, 10 residual blocks and RGB images (c = 3).

Training Settings

Adversarial training along with an objective function consisting of texture matching loss and perceptual loss.

• Perceptual Loss : Difference between the SR output and target in the feature space of a differentiable function (VGG-19 network).
• Texture Matching Loss : For matching the textures, Gram matrix is used as suggested in Gatys et al., (2015)