Gradient-Guided Residual Learning for Inverse Halftoning and Image Expanding

Inverse halftoning and image expanding refer to problems to restore the pixel values of images from compressed images of smaller bit depth. Since these two problems are ill-posed, there are few perfect solutions. Recently, deep convolutional neural networks (DCNN) have shown their powerful ability in inverse halftoning and image expanding. However, the restored images still suffer from visual artifacts or fine details loss due to the improper design of network structure. To this end, this paper proposes a residual learning model for inverse halftoning and image expanding. The whole model consists of two progressive stages. The first stage is a gradient-guided DCNN, which coarsely recovers the main content of the image with the guidance of the predicted gradients. The second stage is a residual network, which learns the residual maps to fine-tune the coarse images, leading better local detail representation. Extensive experiments, including visual quality and numerical evaluation, are performed on the COCO data set. Results show that our method achieves the best performance when compared to the state-of-art methods.