Cascading residual–residual attention generative adversarial network for image super resolution

Image super resolution technology plays an important role in the field of computer vision. With the application of deep learning in the field of image super-resolution, the generative adversarial network is applied to image super-resolution and obtains images with great quality. In this paper, we propose a novel generative adversarial network structure called Cascading Residual–Residual Attention Generative Adversarial Network (CRRAGAN). First, this paper proposes a novel and efficient feature extraction module: Cascading Residual–Residual Block, which can extract multi-scale information and low-level cascade information to high-level information. CRRAGAN directly uses the channel attention module to capture low-resolution image key information and fuse it into the next stage feature. Second, a new loss combination function is proposed, a weighted sum of image loss, adversarial loss, perceptual loss, and charbonnier loss, to make the network training more stable. In the end, we compare our proposed method with 15 previous state-of-the-art methods and discuss the performance of different training datasets. Experimental results demonstrate that our model exhibits improved performance.