Residual Channel Attention Generative Adversarial Network for Image Super-Resolution and Noise Reduction

Abstract

Image super-resolution is one of the important computer vision techniques aiming to reconstruct high-resolution images from corresponding low-resolution ones. Most recently, deep learning based approaches have been demonstrated for image super-resolution. However, as the deep networks go deeper, they become more difficult to train and more difficult to restore the finer texture details, especially under real-world settings. In this paper, we propose a Residual Channel Attention-Generative Adversarial Network (RCA-GAN) to solve these problems. Specifically, a novel residual channel attention block is proposed to form RCA-GAN, which consists of a set of residual blocks with shortcut connections, and a channel attention mechanism to model the interdependence and interaction of the feature representations among different channels. Besides, a generative adversarial network (GAN) is employed to further produce realistic and highly detailed results. Benefiting from these improvements, the proposed RCA-GAN yields consistently better visual quality with more detailed and natural textures than baseline models; and achieves comparable or better performance compared with the state-of-the-art methods for real-world image super-resolution.